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Publication	Type	Year
Identifying Governing ODEs in Irregular Physical Domain with Diffusion 64th Annual Meeting of the APS Division of Plasma Physics Gina Rose Vasey, Kristian Beckwith, Patrick Knapp, William Lewis, Brian O'Shea, Andrew Christlieb, Ravi Ghanshyam Patel, Christopher Ashley Jennings	Abstract – 2022 Abstract	2022
An autoencoder based reduced order model of low density plasma for optimal experimental design APS Division of Plasma Physics Ravi Ghanshyam Patel, William Lewis, Patrick Knapp	Abstract – 2022 Abstract	2022
Continuum semiconductor physics model compression via Data-driven Discrete Exterior Calculus 8th European Congress on Computational Methods in Applied Sciences and Engineering Andy Huang, Nathaniel Albert Trask, Xujiao Gao, Shahed Reza, Ravi Ghanshyam Patel, Christopher Brissette, Xiaozhe Hu	Conference Presentation – 2022 Conference Presentation	2022
Error-in-variables modelling for operator learning AIRES Workshop Ravi Ghanshyam Patel, Indu Manickam, Mamikon Gulian, Myoungkyu Lee	Conference Presentation – 2022 Conference Presentation	2022
Error-in-variables modelling for operator learning Msml22 Ravi Ghanshyam Patel, Indu Manickam, Mamikon Gulian, Myoungkyu Lee	Conference Paper – 2022 Conference Paper	2022
Error-in-variables modelling for operator learning Mathematical and Scientific Machine Learning 2022 (MSML2022) Mamikon Gulian, Ravi Ghanshyam Patel, Indu Manickam, Lee Myoungkyu	Conference Paper – 2022 Conference Paper	2022
Machine learning of Physics-Informed Graph Neural Networks from TCAD models Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (IACM conference) 2021 Andy Huang, Nathaniel Albert Trask, Xujiao Gao, Shahed Reza, Ravi Ghanshyam Patel, Christopher Brissette, Xiaozhe Hu	Conference Presentation – 2021 Conference Presentation	2021
Machine learning surrogates of high-fidelity electrical models REHEDS External Review Andy Huang, Xujiao Gao, Shahed Reza, Nathaniel Albert Trask, Ian Zachary Wilcox, Candace Pauline Diaz, Ravi Ghanshyam Patel	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
A Layer-Parallel Approach for Training Deep Neural Networks Mathematical Foundations and Applications of Deep Learning Eric Christopher Cyr, Stefanie Guenther (LLNL), Lars Ruthotto (Emory), Jacob B. Schroder (UNM), Nico R. Gauger (TU Kaiserslautern), Gordon Moon (KAU), Ravi Ghanshyam Patel	Conference Presentation – 2021 Conference Presentation	2021
Modal Operator Regression for Extracting Nonlocal Continuum Models Usnccm16 Ravi Ghanshyam Patel, Nathaniel Albert Trask, Mitchell Wood, Eric Christopher Cyr	Conference Presentation – 2021 Conference Presentation	2021
A Layer-Parallel Approach for Training Deep Neural Networks Fomics-dadsi Seminars On Scientific Learning Eric Christopher Cyr, Stefanie Guenther (LLNL), Lars Ruthotto (Emory), Jacob B. Schroder (UNM), Nico R. Gauger (TU Kaiserslautern), Gordon Moon (KAU), Ravi Ghanshyam Patel https://www.osti.gov/search/identifier:1872882	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
An Adaptive Basis Perspective to Improve Initialization and Accelerate Training of DNNs Fomics-dadsi Seminars On Scientific Learning Eric Christopher Cyr, Mamikon Gulian, Kookjin Lee (ASU), Ravi Ghanshyam Patel, Mauro Perego, Nathaniel Albert Trask https://www.osti.gov/search/identifier:1872708	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Multiscale training for Physics-informed Neural Networks Copper Mountain Conference On Multigrid Methods Ravi Ghanshyam Patel, Nathaniel Albert Trask, Eric Christopher Cyr https://www.osti.gov/search/identifier:1861255	Conference Presentation – 2021 Conference Presentation	2021
A block coordinate descent optimizer for classification problems exploiting convexity Aaai-mlps 2021 Mamikon Gulian, Ravi Ghanshyam Patel, Nathaniel Albert Trask, Eric Christopher Cyr https://www.osti.gov/search/identifier:1855748	Conference Paper – 2021 Conference Paper	2021
A Physics-Informed Operator Regression Framework for Extracting Data-Driven Continuum Models Siam Cse21 Ravi Ghanshyam Patel, Nathaniel Albert Trask, Mitchell Wood, Eric Christopher Cyr https://www.osti.gov/search/identifier:1853856	Conference Presentation – 2021 Conference Presentation	2021
Thermodynamically consistent physics-informed neural networks for hyperbolic systems SIAM Conference on Computational Science and Engineering Indu Manickam, Ravi Ghanshyam Patel, Nathaniel Albert Trask, Mitchell Wood, Myoungkyu NMN Lee, Ignacio Tomas, Eric Christopher Cyr https://www.osti.gov/search/identifier:1847572	Conference Presentation – 2021 Conference Presentation	2021
An Adaptive Basis Perspective to Improve Initialization and Accelerate Training of DNNs SIAM Conference on Computational Science and Engineering (CSE21) Eric Christopher Cyr, Mamikon Gulian, Ravi Ghanshyam Patel, Mauro Perego, Nathaniel Albert Trask https://www.osti.gov/search/identifier:1847582	Conference Presentation – 2021 Conference Presentation	2021
Control volume PINNs: a method for solving inverse problems with hyperbolic PDEs CRUNCH seminar Ravi Ghanshyam Patel https://www.osti.gov/search/identifier:1840510	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Partition of unity networks: data-driven meshfree hp-approximation AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences Lee Kookjin, Nathaniel Albert Trask, Ravi Ghanshyam Patel, Mamikon Gulian, Eric Christopher Cyr https://www.osti.gov/search/identifier:1835970	Conference Paper – 2020 Conference Paper	2020
Learning continuum-scale models from micro-scale dynamics via Operator Regression 14th World Congress in Computational Mechanics Ravi Ghanshyam Patel, Nathaniel Albert Trask, Mitchell Wood, Eric Christopher Cyr https://www.osti.gov/search/identifier:1834283	Conference Presentation – 2020 Conference Presentation	2020
PDE discovery with machine learning University of New Mexico Applied Math Seminar Ravi Ghanshyam Patel	Presentation (non-conference) – 2020 Presentation (non-conference)	2020
Robust Training and Initialization of Deep Neural Networks An Adaptive Basis Viewpoint Princeton MSML meeting Eric Christopher Cyr, Mamikon Gulian, Ravi Ghanshyam Patel, Mauro Perego, Nathaniel Albert Trask https://www.osti.gov/search/identifier:1810691	Conference Paper – 2020 Conference Paper	2020
Robust training and initialization of deep neural networks: an adaptive basis viewpoint 2020 Siam-caims An20 Mamikon Gulian, Eric Christopher Cyr, Ravi Ghanshyam Patel, Mauro Perego, Nathaniel Albert Trask https://www.osti.gov/search/identifier:1808257	Conference Paper – 2020 Conference Paper	2020
A block coordinate descent optimizer for classification problems exploiting convexity Arxiv Ravi Ghanshyam Patel, Nathaniel Albert Trask, Mamikon Gulian, Eric Christopher Cyr https://www.osti.gov/search/identifier:1834091	Journal Article – 2020 Journal Article	2020
A physics-informed operator regression framework for extracting data-driven continuum models Arxiv Ravi Ghanshyam Patel, Nathaniel Albert Trask, Mitchell Wood, Eric Christopher Cyr https://www.osti.gov/search/identifier:1725869	Journal Article – 2020 Journal Article	2020
Document Title	Type	Year

Results 1–25 of 32

Publications

Identifying Governing ODEs in Irregular Physical Domain with Diffusion

An autoencoder based reduced order model of low density plasma for optimal experimental design

Continuum semiconductor physics model compression via Data-driven Discrete Exterior Calculus

Error-in-variables modelling for operator learning

Error-in-variables modelling for operator learning

Error-in-variables modelling for operator learning

Machine learning of Physics-Informed Graph Neural Networks from TCAD models

Machine learning surrogates of high-fidelity electrical models

A Layer-Parallel Approach for Training Deep Neural Networks

Modal Operator Regression for Extracting Nonlocal Continuum Models

A Layer-Parallel Approach for Training Deep Neural Networks

An Adaptive Basis Perspective to Improve Initialization and Accelerate Training of DNNs

Multiscale training for Physics-informed Neural Networks

A block coordinate descent optimizer for classification problems exploiting convexity

A Physics-Informed Operator Regression Framework for Extracting Data-Driven Continuum Models

Thermodynamically consistent physics-informed neural networks for hyperbolic systems

An Adaptive Basis Perspective to Improve Initialization and Accelerate Training of DNNs

Control volume PINNs: a method for solving inverse problems with hyperbolic PDEs

Partition of unity networks: data-driven meshfree hp-approximation

Learning continuum-scale models from micro-scale dynamics via Operator Regression

PDE discovery with machine learning

Robust Training and Initialization of Deep Neural Networks An Adaptive Basis Viewpoint

Robust training and initialization of deep neural networks: an adaptive basis viewpoint

A block coordinate descent optimizer for classification problems exploiting convexity

A physics-informed operator regression framework for extracting data-driven continuum models