Publications

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A Two-Level Scheme for Training Partition of Unity Networks

27th International Domain Decomposition Conference

Eric Christopher Cyr, Nathaniel Albert Trask, Amelia Henriksen, Carianne Martinez

Conference Presentation – 2022 Conference Presentation 2022

Accurate Compression of Tabulated Chemistry Models with Partition of Unity Networks

Combustion Science and Technology

Elizabeth Armstrong, Michael Alan Hansen, Robert C. Knaus, Nathaniel Albert Trask, John C. Hewson, James Sutherland

Journal Article – 2022 Journal Article 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

Scalable algorithms for physics-informed neural and graph networks

Data Centric Engineering

Khemraj Shukla, Mengjia Xu, Nathaniel Albert Trask, George Karniadakis

https://www.osti.gov/search/identifier:1872036

Journal Article – 2022 Journal Article 2022

Reinforcement learning for material calibration via kalman filter estimation

USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling

Ruben Villarreal, Thomas Anthony Catanach, Reese E. Jones, Nathaniel Albert Trask, Sharlotte LorraineBolyard Kramer, Nikolaos Vlassis N., WaiChing Sun

Abstract – 2022 Abstract 2022

Improving chemistry tabulation with partition of unity networks

18th International Conference on Numerical Combustion

Elizabeth Armstrong, Michael Alan Hansen, Robert C. Knaus, Nathaniel Albert Trask, John C. Hewson, James Sutherland

Conference Presentation – 2022 Conference Presentation 2022

Structure preserving machine learning for data-driven multiscale/multiphysics modeling

Stanford engineering seminar

Nathaniel Albert Trask

Presentation (non-conference) – 2022 Presentation (non-conference) 2022

Improving Chemistry Tabulation with Partition of Unity Networks

18th International Conference on Numerical Combustion

Elizabeth Armstrong, Michael Alan Hansen, Robert C. Knaus, Nathaniel Albert Trask, John C. Hewson, James Sutherland

Abstract – 2022 Abstract 2022

Physics-informed Multimodal Autoencoders (PIMA) High-throughput science through modal fusion

crunch webinar

Nathaniel Albert Trask

Presentation (non-conference) – 2022 Presentation (non-conference) 2022

Accurate Compression of Tabulated Chemistry Models with Partition of Unity Networks

39th International Symposium on Combustion

Elizabeth Armstrong, Michael Alan Hansen, Robert C. Knaus, Nathaniel Albert Trask, John C. Hewson, James Sutherland

Conference Paper – 2022 Conference Paper 2022

Remapping native fields for climate applications

15th World Congress on Computational Mechanics (WCCM-XV)

Paul Allen Kuberry, Mauro Perego, Nathaniel Albert Trask, Pavel B. Bochev

Abstract – 2021 Abstract 2021

Sandia / IBM Discussion on Machine Learning for Materials Applications

David John Littlewood, Mitchell Wood, David Montes de Oca Zapiain, Sivasankaran Rajamanickam, Nathaniel Albert Trask

https://www.osti.gov/search/identifier:1828106

Report – 2021 Report 2021

Structure preserving machine learning for data-driven multiscale/multiphysics modeling

UPenn Engineering department colloquium

Nathaniel Albert Trask

Presentation (non-conference) – 2021 Presentation (non-conference) 2021

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

Discovery of structure-preserving finite element spaces for multiscale

Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology

Nathaniel Albert Trask

Conference Presentation – 2021 Conference Presentation 2021

ASCEND: Asymptotically compatible strong form foundations for nonlocal discretization

Nathaniel Albert Trask, Marta D'Elia, David John Littlewood, Stewart A. Silling, Jeremy Trageser, Michael R. Tupek

https://www.osti.gov/search/identifier:1820006

SAND Report – 2021 SAND Report 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 data-driven exterior calculus for model discovery

Usacm

Nathaniel Albert Trask

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

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

Structure preserving architectures for SciML

Philms webinar

Nathaniel Albert Trask

https://www.osti.gov/search/identifier:1872183

Presentation (non-conference) – 2021 Presentation (non-conference) 2021

ASCeND: ASymptotically Compatible Strong Form Foundations for Nonlocal Discretization

Sandia Academic Alliance Spring 2021 UT Austin LDRD Virtual Poster Session

Barun Das, Masoud Behzadinasab, Nathaniel Albert Trask, John Foster

https://www.osti.gov/search/identifier:1862184

Conference Poster – 2021 Conference Poster 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

Greedy Fiedler Spectral Partitioning for Data-driven Discrete Exterior Calculus

Association for the Advancement of Artificial Intelligence - Machine Learning for the Physical Sciences (AAAI-MLPS) 2021

Andy Huang, Nathaniel Albert Trask, Christopher Brissette, Xiaozhe Hu

https://www.osti.gov/search/identifier:1859685

Conference Presentation – 2021 Conference Presentation 2021

Making physics-informed ML work

Informal presentation for ML reading group at LANL

Nathaniel Albert Trask

https://www.osti.gov/search/identifier:1856302

Presentation (non-conference) – 2021 Presentation (non-conference) 2021
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Results 1–25 of 129