2023-07-13T13:02:15Z

Nikos: /* Announcements */

[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]

== Announcements ==

''' Professor Chrisochoides will be teaching (Fall’23) a new class on Quantum Computing; for more details, see [[https://crtc.cs.odu.edu/File:CS495-595_on_QC.png here.]]'''

=== Congratulations to Dr. Thomadakis===
[[File:Polykarpos-Grad.jpeg|200px|left]]
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.

The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.'''

Nuclear Physicists confirmed these results in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow, which adds another $5M per year to the earlier estimate by Dr. Weinstein.

Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.

These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.

=== Protein-to-Protein Interactions ===
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].

A slideshow of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].

== Introduction ==
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-speciﬁc knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for speciﬁc supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]

<center>
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big>
'''
</center>
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]

'''Broader Impact'''
The proposed activity will have a signiﬁcant and broader impact on research and education, well beyond the bounds of this speciﬁc project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will beneﬁt the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US.

'''Intellectual Merit'''
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.

== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==
<gallery heights=300px widths=300px mode="slideshow">
File:telescopic_and_meshes.jpg
File:CDT3D slide rocket nacelle.jpg ‎
File:Dlr airbus.jpg
File:Scitech 2019 delta wing.jpg
</gallery>

<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level">
File:CDT3D.jpg|130px|CDT3D Mesh
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus
File:Output_grid.jpg|130px|Rocket with Engine Mesh
|- style="text-align: center;"
||||'''CDT3D Meshes using speculative execution model at the chip level'''
</gallery>

=== Overview ===
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations.

CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. This is a joined project with NASA/LaRC and MIT. 

<li style="display: inline-block;">
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.

'''Objectives'''
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.
:# Development of error-based metrics to drive an anisotropic adaptive process
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.
</li>

== Real-time Adaptive Mesh Generation in Medical Image Computing ==

This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI.

=== Overview ===
<li style="display: inline-block;">
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. This project is a joined with VCU, VA. 
</li>

<li style="display: inline-block;">
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. This is a joined project with Harvard Medical School, MA. 
</li>

<li style="display: inline-block;">
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. This is a joined project with Medical School at Stony Brook University, NY. 
</li>

File:CS495-595 on QC.png

2023-07-13T13:01:42Z

Nikos: Nikos uploaded a new version of File:CS495-595 on QC.png

Main Page

2023-07-12T19:50:42Z

Nikos: /* Announcements */

[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]

== Announcements ==

''' Professor Chrisochoides will be teaching (Fall’23) a new class on Quantum Computing; for more details, see [[https://crtc.cs.odu.edu/images/8/85/CS495-595_on_QC.png here.]]'''

=== Congratulations to Dr. Thomadakis===
[[File:Polykarpos-Grad.jpeg|200px|left]]
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.

The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.'''

Nuclear Physicists confirmed these results in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow, which adds another $5M per year to the earlier estimate by Dr. Weinstein.

Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.

These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.

=== Protein-to-Protein Interactions ===
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].

A slideshow of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].

== Introduction ==
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-speciﬁc knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for speciﬁc supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]

<center>
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big>
'''
</center>
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]

'''Broader Impact'''
The proposed activity will have a signiﬁcant and broader impact on research and education, well beyond the bounds of this speciﬁc project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will beneﬁt the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US.

'''Intellectual Merit'''
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.

== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==
<gallery heights=300px widths=300px mode="slideshow">
File:telescopic_and_meshes.jpg
File:CDT3D slide rocket nacelle.jpg ‎
File:Dlr airbus.jpg
File:Scitech 2019 delta wing.jpg
</gallery>

<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level">
File:CDT3D.jpg|130px|CDT3D Mesh
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus
File:Output_grid.jpg|130px|Rocket with Engine Mesh
|- style="text-align: center;"
||||'''CDT3D Meshes using speculative execution model at the chip level'''
</gallery>

=== Overview ===
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations.

CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. This is a joined project with NASA/LaRC and MIT. 

<li style="display: inline-block;">
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.

'''Objectives'''
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.
:# Development of error-based metrics to drive an anisotropic adaptive process
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.
</li>

== Real-time Adaptive Mesh Generation in Medical Image Computing ==

This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI.

=== Overview ===
<li style="display: inline-block;">
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. This project is a joined with VCU, VA. 
</li>

<li style="display: inline-block;">
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. This is a joined project with Harvard Medical School, MA. 
</li>

<li style="display: inline-block;">
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. This is a joined project with Medical School at Stony Brook University, NY. 
</li>

Main Page

2023-07-12T18:03:34Z

Nikos: /* Announcements */

[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]

== Announcements ==

''' Professor Chrisochoides will be teaching a new class on Quantum Computing; for more details, see [[https://crtc.cs.odu.edu/images/8/85/CS495-595_on_QC.png here.]]'''

=== Congratulations to Dr. Thomadakis===
[[File:Polykarpos-Grad.jpeg|200px|left]]
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.

The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. '''Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.'''

Nuclear Physicists confirmed these results in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization acknowledged in a recent article ([https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis]) that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables from 35% to approximately 50-80% by adding the AI-based denoising ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] ) of the data in the workflow, which adds another $5M per year to the earlier estimate by Dr. Weinstein.

Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.

These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.

=== Protein-to-Protein Interactions ===
Progress on protein-to-protein interactions using geometric feature analysis: [[COVID-19]].

A slideshow of Molecular Geometric Feature Analysis for SARS-CoV-2: click [[Molecular Geometric Feature Analysis | here]].

== Introduction ==
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-speciﬁc knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for speciﬁc supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]]. [https://patents.google.com/?inventor=Nikos+CHRISOCHOIDES Six patent applications were filled.]

<center>
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big>
'''
</center>
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]

'''Broader Impact'''
The proposed activity will have a signiﬁcant and broader impact on research and education, well beyond the bounds of this speciﬁc project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will beneﬁt the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US.

'''Intellectual Merit'''
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.

== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==
<gallery heights=300px widths=300px mode="slideshow">
File:telescopic_and_meshes.jpg
File:CDT3D slide rocket nacelle.jpg ‎
File:Dlr airbus.jpg
File:Scitech 2019 delta wing.jpg
</gallery>

<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level">
File:CDT3D.jpg|130px|CDT3D Mesh
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus
File:Output_grid.jpg|130px|Rocket with Engine Mesh
|- style="text-align: center;"
||||'''CDT3D Meshes using speculative execution model at the chip level'''
</gallery>

=== Overview ===
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations.

CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. This is a joined project with NASA/LaRC and MIT. 

<li style="display: inline-block;">
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.

'''Objectives'''
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.
:# Development of error-based metrics to drive an anisotropic adaptive process
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.
</li>

== Real-time Adaptive Mesh Generation in Medical Image Computing ==

This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI.

=== Overview ===
<li style="display: inline-block;">
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. This project is a joined with VCU, VA. 
</li>

<li style="display: inline-block;">
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. This is a joined project with Harvard Medical School, MA. 
</li>

<li style="display: inline-block;">
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. This is a joined project with Medical School at Stony Brook University, NY. 
</li>

2023-05-12T20:07:48Z

Nikos: /* Congratulations to Dr. Thomadakis */

[[File:Thanksgiving lunch 2019.jpg|600px|thumb|frameless|center| Chrisochoides' Real-Time Computing (or in short CRTC) group before the pandemic.]]

== Announcements ==

=== Congratulations to Dr. Thomadakis===
[[File:Polykarpos-Grad.jpeg|200px|left]]
Congratulations to Dr. Thomadakis for his impressive Ph.D. work on parallel intelligent runtime software systems for HPC adaptive and irregular applications and Domain-Specific Languages.

The AI part of his PhD work ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=De-noising%20drift%20chambers%20in%20CLAS12%20using%20convolutional%20auto%20encoders&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Using%20Machine%20Learning%20for%20Particle%20Track%20Identification%20in%20the%20CLAS12%20Detector&paper_type=2&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Track%20Identification%20for%20CLAS12%20using%20Artificial%20Intelligence&paper_type=1&from=year]) was completed in collaboration with [https://www.linkedin.com/in/gagik-gavalian-55926725/ Dr. Gagik Gavalian] from [https://www.jlab.org/ Jefferson Lab] and other former members of the [https://crtc.cs.odu.edu/Main_Page CRTC], including current member Kevin Garner. Some of the findings from his research is projected to have a significant impact over the next decade, with a value of approximately $100 million.

These results were confirmed by Nuclear Physicists in a [https://www.odu.edu/article/ai-collaboration-between-odu-jefferson-lab-improves-data-analysis recent article], and Dr. Gavalian presented the findings in [https://indico.jlab.org/event/459/contributions/11745/attachments/9491/13757/CHEP-05-2023-Gavalian.pdf CHEP'23]. [https://www.odu.edu/directory/lawrence-weinstein Dr. Lawrence Weinstein], an ODU Eminent Scholar, professor of physics, and past chair of the Jefferson Lab Users Organization, acknowledged that the project has improved particle trajectory reconstruction, resulting in a 35% increase in the number of reconstructed complicated nuclear collisions. This means that "more physics from the same data" is possible, which would have otherwise cost about $5 million per year. Dr. Gavalian's new results show that there has been an increase in statistics for physics observables of approximately 50-80%.

Next, Dr. Gagik will be looking into applying lessons learned to other Jefferson Lab Halls (sensors) and we will be putting this AI work on “steroids” using scalable D-NISQ Quantum Machine Learning. Our preliminary results ([https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Evaluation%20of%20Scalable%20Quantum%20and%20Classical%20Machine%20Learning%20for%20Particle%20Tracking%20Classification%20in%20Nuclear%20Physics&paper_type=1&from=year] [https://crtc.cs.odu.edu/pub/html_output/details.php?&title=Scalable%20Quantum%20Edge%20Detection%20Method%20for%20D-NISQ%20Imaging%20Simulations:%20Use%20Cases%20from%20Nuclear%20Physics%20and%20Medical%20Image%20Computing&paper_type=1&from=year]) in Quantum Computing indicate that this is feasible.

These accomplishments would not have been possible without the small investment from Jefferson Lab ($100K) and the Richard T. Cheng Endowment ($50K), as well as the help of many others, including the Dragas Foundation, which has assisted Olga Karadimou, an exceptional Ph.D. student at ODU, in staying at ODU with Polykarpos.

=== COVID-19 ===
Check our work in progress on protein-to-protein interactions using Geometric Feature Analysis: [[COVID-19]].

== Introduction ==
Finite Element (FE) mesh generation is a critical component for many (bio-)engineering and science applications. CRTCLab is developing a novel framework for highly scalable and energy-efficient high-quality mesh generation for the Finite Element Analysis in three and four dimensions. CRTC effort and focus is on research activities that combine domain-and application-speciﬁc knowledge with run-time system support to improve energy efficiency and scalability of parallel FE mesh generation codes. Traditionally, parallel FE mesh generation methods and software are developed without considering the architectural features of the supercomputer platforms on which they are eventually used for production. The main reason is the complexity of sequential, and moreover parallel, mesh generation algorithms. As a result, it is too expensive, in terms of labor and time, to customize the performance of parallel mesh generation software for speciﬁc supercomputing architectures. CRTC’s basic research is funded from NSF, NASA, NRA, total $2M and target (bio-)engineering applications are: [[#Extreme-Scale_Parallel_Mesh_Generation_&_Adaptation | Aerospace Industry Applications]] and [[#Medical_Image_Computing | Health Care Industry Applications]].

<center>
'''<big>Principles for Building Practical and Scalable Domain-Specific Environment: Adaptive Data-Driven Mesh Generation for Modeling & Simulations</big>
'''
</center>
[[File:Adaptivity PPoSS.png|thumb|center|600px| This graph indicates the core enabling technologies we develop at CRTC: (i) Adaptive Tessellation Techniques: Theory and Systems and the corresponding and (ii) Parallel Runtime Software Systems required to run on current and emerging Supercomputers. The pictures around indicate the five main applications we target from left to right: (a) Computational Fluid Dynamics, (b) Numerical Flow Simulations used to Predict Flow Diversion Treatment Efficacy of Cerebral Aneurysms, (c) Trauma Brain Injuries, (d) Physic Based Adaptive Deformable Registration used in Image-Guided Neurosurgery, and (e) Imaging for Nuclear Femtography with our collaborators: from NASA/LARC, Stony Brook Medical School, Navy Research Lab, Harvard Medical School, and Physics and Jefferson Lab, respectively. The common denominator for all these applications is adaptive parallel tessellation ( or known in the science and engineering community as mesh generation or image-to-mesh conversion). Parallel mesh generation is "a new research area between the boundaries of two [https://en.wikipedia.org/wiki/Scientific_computing scientific computing] disciplines: [https://en.wikipedia.org/wiki/Computational_geometry computational geometry] and [https://en.wikipedia.org/wiki/Parallel_computing parallel computing]" [https://en.wikipedia.org/wiki/Parallel_mesh_generation]. The applications are listed (left to right) with respect to interactions of input form Computer-Aided Design to Sensor data like MRI, US, etc.]]

'''Broader Impact'''
The proposed activity will have a signiﬁcant and broader impact on research and education, well beyond the bounds of this speciﬁc project and its external collaborators. Other than the direct impact to parallel unstructured mesh generation and runtime systems for supercomputers like Blue Waters, this project will beneﬁt the performance evaluation community. This project will also promote the understanding on CerebroVascular Disease (CVD, or stroke), Image Guided Surgery for Caner, and DBS for Parkisons disease, all of which are leading natural death causes in the US.

'''Intellectual Merit'''
The Intellectual Merit of this project is summarized in terms of three novel contributions: (1) an understanding of the complex computation, communication, and memory utilization patterns in a telescopic superposition of multiple parallel mesh generation codes, (2) new abstractions to efficiently throttle concurrency and to implement race-to-halt execution models at multiple levels of granularity, while orchestrating intra-and inter-layer memory management, data movement, and load balancing to improve energy-efficiency of memory and interconnect components, and (3) a novel approach to leveraging off-the-shelf and state-of-the-art low core meshing codes for highly parallel simulations.

== Extreme-Scale Adaptive/Anisotropic Parallel Mesh Generation ==
<gallery heights=300px widths=300px mode="slideshow">
File:telescopic_and_meshes.jpg
File:CDT3D slide rocket nacelle.jpg ‎
File:Dlr airbus.jpg
File:Scitech 2019 delta wing.jpg
</gallery>

<gallery heights=200px widths=200px mode="packed-hover" caption="CDT3D Meshes using speculative execution model at the chip level">
File:CDT3D.jpg|130px|CDT3D Mesh
File:Rocket_Mesh.jpg|130px|DLR-F6 Airbus
File:Output_grid.jpg|130px|Rocket with Engine Mesh
|- style="text-align: center;"
||||'''CDT3D Meshes using speculative execution model at the chip level'''
</gallery>

=== Overview ===
Finite Element Mesh Generation is a critical component for many (bio-) engineering and science applications. The goal of this project is to deliver a novel Telescopic framework for highly scalable and energy efficient codes. Domain-and application-specific knowledge and run-time system support are combined to improve accuracy of FE computations.

CRTCLab addresses in depth two pillars: (i) high performance computing software runtime systems and (ii) mesh generation & adaptation based on error-metrics for NASA’s Computational Fluid Dynamics (CFD) Vision 2030. Specifically, CRTC’s focus is on the following three objectives: (1) design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation using state-of-the-art functionality supported by algorithms from AFLR and CRTC’s telescopic approach for parallel mesh generation , (2) develop error-based metrics adaptive anisotropic FE mesh generation methods and (3) design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretizations with localizable error estimates. This is a joined project with NASA/LaRC and MIT. 

<li style="display: inline-block;">
[[File: SubGroup1.jpg|thumb|right|500px| '''CRTC's sub-group for Extreme Scale Parallel Mesh Generation & Adaptation, from left to right: Nikos Chrisochoides, Kevin Garner, Dana Hammond (former TM from NASA/LaRC), Christos Tsolakis and Polykarpos Thomadakis. ''']]
We have assembled a team of established leaders (see [https://cepm.cs.odu.edu/People#External_Collaborators External Collaborators]) that are currently developing state-of-the-art work on mesh generation and adaptivity issues relevant to NASA’s CFD 2030 Vision and will broadly impact end-user productivity of users throughout DoD and NASA.

'''Objectives'''
:# Design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation methods using state-of-the-art functionality supported by methods implemented in AFLR and CRTC’s telescopic approach for parallel mesh generation.
:# Development of error-based metrics to drive an anisotropic adaptive process
:# Design a power-aware parallel runtime software system for extreme-scale adaptive CFD computations including: (i) mesh generation & adaptation, and (ii) consistent error-based metrics for adaptation of any CFD discretization with localizable error estimates.
</li>

== Real-time Adaptive Mesh Generation in Medical Image Computing ==

This application is using adaptive (isotropic) mesh refinement to improve (in real-time in the context of image-guided neurosurgery) the accuracy of 3D Physics-Based Deformable Registration of intra-operative MRI with pre-operative MRI.

=== Overview ===
<li style="display: inline-block;">
[[File: CT imaging.jpg|thumb|right|500px| '''Deep Brain Stimulation – CT Imaging obtained from Neurosurgical Section of SE PADRECC McGuire VAMC (with VCU)''']]
:* Develop adaptive deformable registration method for Deep Brain Stimulation (DBS). DBS is an effective palliative therapy for patients suffering from Essential Tremor, Parkinson’s disease, and other neurological movement disorders. Modern DBS surgery makes use of stereotactic systems and image guidance to accurately place electrode leads, as well as intra-operative imaging to surveil the location of the leads and guide the surgery. The effectiveness of DBS is directly correlated with the accuracy of DBS electrode lead placement, with more accurate electrode placement leading to better clinical outcomes. This project is a joined with VCU, VA. 
</li>

<li style="display: inline-block;">
[[File: Health-care.jpg|thumb|left|500px| '''APBNRR ''']]
:* Develop a new Adaptive Physics-Based Non-Rigid Registration (APBNRR) method developed for image guided brain tumor resection. Existing evaluation indicates that the the registration accuracy and performance of APBNRR are proved sufficient to be of clinical value in the operating room. Malignant gliomas are the most common primary and metastatic brain tumors. Treatment typically includes surgical removal followed by radiotherapy or chemotherapy. Total resection is difficult to achieve during tumor removal because of the infiltrative nature of gliomas and because brain tumors are often embedded in critical functional brain tissue. In image-guided neurosurgery, co-registered pre-operative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in certain areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. This is a joined project with Harvard Medical School, MA. 
</li>

<li style="display: inline-block;">
[[File: ExtremeScale.jpg|thumb|right|500px| '''Images created by the I2M tool using the CBC3D Method''']]
:* Test five new available (in the clinic) neurovascular devices that have not been compared before through angiographic washout analysis and accurate micro CT-based tessellation (i.e., mesh or grid reconstruction) and CFD analysis in idealized sidewall aneurysm models. The CFD simulations and angiographic washout analysis will be performed on flow diverter implantations in actual patient geometries. There is a need for robust and easy-to-use Image-to-Mesh (I2M) conversion methods and software that streamline the discretization of such complex vascular geometries with fine structures with the CFD modeling process. In this project we will focus on the development and validation of fully functional I2M conversion algorithms and software (i.e., tools) for complex vascular geometries with fine structures like stents. This is a joined project with Medical School at Stony Brook University, NY. 
</li>