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Publication	Type	Year
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
Physics-informed graph neural nets A unification of NN architectures with mimetic PDE discretization SIAM Annual meeting (virtual) Nathaniel Albert Trask https://www.osti.gov/search/identifier:1808025	Conference Paper – 2020 Conference Paper	2020
Physics-Informed Graph Neural Network for Circuit Compact Model Development Sispad 2020 Xujiao Gao, Andy Huang, Nathaniel Albert Trask, Shahed Reza	Abstract – 2020 Abstract	2020
Physics-informed graph neural nets (pigNNS) A unification of NN architectures with mimetic PDE discretization Tufts data science webinar Nathaniel Albert Trask https://www.osti.gov/search/identifier:1798063	Presentation (non-conference) – 2020 Presentation (non-conference)	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
Data-driven learning of robust nonlocal physics from high-fidelity synthetic data Arxiv Huaiqian You, Yue Yu, Nathaniel Albert Trask, Mamikon Gulian, Marta D'Elia https://www.osti.gov/search/identifier:1765754	Journal Article – 2020 Journal Article	2020
A unified, higher-order meshfree framework for peridynamic correspondence modeling. Part I: development of a stable formulation in the strong form Arxiv Masoud Behzadinasab, Yuri Bazilevs, Nathaniel Albert Trask https://www.osti.gov/search/identifier:1668361	Journal Article – 2020 Journal Article	2020
Interface Flux Recovery Coupling Method for the Ocean-Atmosphere System Results in Applied Mathematics Kenneth Chadwick Sockwell, Kara J. Peterson, Paul Allen Kuberry, Pavel B. Bochev, Nathaniel Albert Trask https://www.osti.gov/search/identifier:1619234	Journal Article – 2020 Journal Article	2020
GMLS-Nets: A machine learning framework for unstructured data 2020 AAAI SPRING SYMPOSIUM (Virtual meeting) Nathaniel Albert Trask, Ravi Ghanshyam Patel, Ben Gross, Paul Atzberger https://www.osti.gov/search/identifier:1773276	Conference Paper – 2020 Conference Paper	2020
Compatible meshfree discretization UIUC Engineering Colloquium Nathaniel Albert Trask https://www.osti.gov/search/identifier:1773275	Presentation (non-conference) – 2020 Presentation (non-conference)	2020
Physics-Informed, Multi-Stage Machine Learning for Circuit Compact Models SIAM Conference on Mathematics of Data Science (MDS20) Andy Huang, Nathaniel Albert Trask, Xujiao Gao, Shahed Reza	Abstract – 2020 Abstract	2020
Field reconstruction from Raviart-Thomas, Nedelec, and finite volume degrees-of-freedom using Generalized Moving Least Squares Joint Mathematics Meeting Paul Allen Kuberry, Mauro Perego, Nathaniel Albert Trask, Pavel B. Bochev https://www.osti.gov/search/identifier:1761038	Conference Paper – 2020 Conference Paper	2020
Asymptotically compatible reproducing kernel collocation and meshfree integration for the peridynamic Navier equation Computer methods in applied mechanics and engineering Yu Leng, Xiaochuan Tian, Nathaniel Albert Trask, John Foster https://www.osti.gov/search/identifier:1670723	Journal Article – 2020 Journal Article	2020
Improved Neural Network Training: Layer-Parallelism, Least-squares and Initialization International Workshop On Scientific Machine Learning Eric Christopher Cyr, Mamikon Gulian, Ravi Ghanshyam Patel, Mauro Perego, Nathaniel Albert Trask, Denis Ridzal, Stefanie Guenther (LLNL), Lars Ruthotto (Emory), Jacob B. Schroder (UNM), Nico R. Gauger (TU Kaiserslautern) https://www.osti.gov/search/identifier:1643364	Conference Paper – 2019 Conference Paper	2019
GMLS-Nets: Scientific Machine Learning Methods for Unstructured Data Neural Information Processing Systems Nathaniel Albert Trask, Ravi Ghanshyam Patel, Ben Gross, Paul Atzberger https://www.osti.gov/search/identifier:1643425	Conference Paper – 2019 Conference Paper	2019
Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint Mathematical and Scientific Machine Learning Conference Eric Christopher Cyr, Mamikon Gulian, Ravi Ghanshyam Patel, Mauro Perego, Nathaniel Albert Trask https://www.osti.gov/search/identifier:1643463	Conference Paper – 2019 Conference Paper	2019
Improved Neural Network Training: Layer-Parallelism, Least-squares and Initialization International Workshop on Scientific Machine Learning Eric Christopher Cyr, Mamikon Gulian, Ravi Ghanshyam Patel, Mauro Perego, Nathaniel Albert Trask, Stefanie (LLNL) Guenther, Lars (Emory University) Ruthotto, Jacob B Schroder, (UNM), Nico R. (TU Kaiserslautern) Gauger	Abstract – 2019 Abstract	2019
Compatible meshfree discretization Tufts applied math seminar Nathaniel Albert Trask https://www.osti.gov/search/identifier:1646410	Presentation (non-conference) – 2019 Presentation (non-conference)	2019
Physics informed machine learning at SNL LANL seminar Nathaniel Albert Trask, Andy Huang https://www.osti.gov/search/identifier:1646432	Presentation (non-conference) – 2019 Presentation (non-conference)	2019
Bulk-Interface-Flux-Recovery (Bulk-IFR) Method for Coupling the Atmosphere-Ocean Interface Conference on Computational Mathematics and Applications Kenneth Chadwick Sockwell, Paul Allen Kuberry, Pavel B. Bochev, Nathaniel Albert Trask, Kara J. Peterson https://www.osti.gov/search/identifier:1642998	Conference Paper – 2019 Conference Paper	2019
Spatially compatible meshfree discretization through GMLS and graph theory Lehigh Math Department Seminar Nathaniel Albert Trask https://www.osti.gov/search/identifier:1675161	Presentation (non-conference) – 2019 Presentation (non-conference)	2019
Stochastic Discontinuous Galerkin Methods based on fluctuation-dissipation balance Results in applied mathematics Will Pazner, Nathaniel Albert Trask, Paul Atzberger https://www.osti.gov/search/identifier:1574443	Journal Article – 2019 Journal Article	2019
A spatially adaptive high-order meshless method for fluid-structure interactions Cmame Wei Hu, Nathaniel Albert Trask, Xiaozhe Hu, Wenxiao Pan https://www.osti.gov/search/identifier:1570255	Journal Article – 2019 Journal Article	2019
Compatible Particle Discretizations. Final LDRD Report Pavel B. Bochev, Peter Andrew Bosler, Paul Allen Kuberry, Mauro Perego, Kara J. Peterson, Nathaniel Albert Trask https://www.osti.gov/search/identifier:1569143	SAND Report – 2019 SAND Report	2019
Document Title	Type	Year

Results 51–75 of 129

Publications

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

Physics-informed graph neural nets A unification of NN architectures with mimetic PDE discretization

Physics-Informed Graph Neural Network for Circuit Compact Model Development

Physics-informed graph neural nets (pigNNS) A unification of NN architectures with mimetic PDE discretization

A block coordinate descent optimizer for classification problems exploiting convexity

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

Data-driven learning of robust nonlocal physics from high-fidelity synthetic data

A unified, higher-order meshfree framework for peridynamic correspondence modeling. Part I: development of a stable formulation in the strong form

Interface Flux Recovery Coupling Method for the Ocean-Atmosphere System

GMLS-Nets: A machine learning framework for unstructured data

Compatible meshfree discretization

Physics-Informed, Multi-Stage Machine Learning for Circuit Compact Models

Field reconstruction from Raviart-Thomas, Nedelec, and finite volume degrees-of-freedom using Generalized Moving Least Squares

Asymptotically compatible reproducing kernel collocation and meshfree integration for the peridynamic Navier equation

Improved Neural Network Training: Layer-Parallelism, Least-squares and Initialization

GMLS-Nets: Scientific Machine Learning Methods for Unstructured Data

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

Improved Neural Network Training: Layer-Parallelism, Least-squares and Initialization

Compatible meshfree discretization

Physics informed machine learning at SNL

Bulk-Interface-Flux-Recovery (Bulk-IFR) Method for Coupling the Atmosphere-Ocean Interface

Spatially compatible meshfree discretization through GMLS and graph theory

Stochastic Discontinuous Galerkin Methods based on fluctuation-dissipation balance

A spatially adaptive high-order meshless method for fluid-structure interactions

Compatible Particle Discretizations. Final LDRD Report