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Publication	Type	Year
Facilitating Atmospheric Source Inversion via Operator Regression SIAM Conference on Mathematics of Planet Earth Joseph Lee Hart, Mamikon Gulian, Indu Manickam, Laura Painton Swiler	Conference Presentation – 2022 Conference Presentation	2022
Facilitating Atmospheric Source Inversion via Deep Operator Network Surrogates Esco 2022 Mamikon Gulian, Joseph Lee Hart, Indu Manickam, Laura Painton Swiler	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
Gaussian Process Regression constrained by Boundary Value Problems Computer Methods in Applied Mechanics and Engineering Mamikon Gulian, Ari Louis Frankel, Laura Painton Swiler https://www.osti.gov/search/identifier:1831157	Journal Article – 2021 Journal Article	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
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
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
Constrained Gaussian Processes: A Survey SIAM Computational Science and Engineering Conference 2021 Laura Painton Swiler, Mamikon Gulian, Ari Louis Frankel, Cosmin Safta, John Davis Jakeman https://www.osti.gov/search/identifier:1847480	Conference Presentation – 2021 Conference Presentation	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
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges Journal of Machine Learning for Modeling and Computing Laura Painton Swiler, Mamikon Gulian, Ari Louis Frankel, Cosmin Safta, John Davis Jakeman https://www.osti.gov/search/identifier:1691455	Journal Article – 2020 Journal Article	2020
LDRD Project Summary: Incorporating physical constraints into Gaussian process surrogate models Laura Painton Swiler, Mamikon Gulian, Ari Louis Frankel, John Davis Jakeman, Cosmin Safta https://www.osti.gov/search/identifier:1668928	SAND Report – 2020 SAND Report	2020
Constrained Gaussian Processes: A Survey SIAM Computational Science and Engineering 2021 Laura Painton Swiler, Mamikon Gulian, Ari Louis Frankel, Cosmin Safta, John Davis Jakeman	Abstract – 2020 Abstract	2020
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges PhILMs Webinar Mamikon Gulian, Laura Painton Swiler, Ari Louis Frankel, Cosmin Safta, John Davis Jakeman https://www.osti.gov/search/identifier:1814448	Conference Paper – 2020 Conference Paper	2020
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges Sandia Machine Learning and Deep Learning Workshop Skip to end of banner Mamikon Gulian, Laura Painton Swiler, Ari Louis Frankel, John Davis Jakeman, Cosmin Safta https://www.osti.gov/search/identifier:1812282	Conference Paper – 2020 Conference Paper	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
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges Machine Learning and Deep Learning Conference (Sandia Audience) Mamikon Gulian, Laura Painton Swiler, Ari Louis Frankel, Cosmin Safta, John Davis Jakeman	Abstract – 2020 Abstract	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 Survey of Constrained Gaussian Process: Approaches and Implementation Challenges Journal of Machine Learning for Modeling and Computing Laura Painton Swiler, Mamikon Gulian, Ari Louis Frankel, Cosmin Safta, John Davis Jakeman https://www.osti.gov/search/identifier:1725870	Journal Article – 2020 Journal Article	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
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
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
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
Document Title	Type	Year

Results 1–25 of 27

Publications

Facilitating Atmospheric Source Inversion via Operator Regression

Facilitating Atmospheric Source Inversion via Deep Operator Network Surrogates

Error-in-variables modelling for operator learning

Error-in-variables modelling for operator learning

Error-in-variables modelling for operator learning

Gaussian Process Regression constrained by Boundary Value Problems

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

A block coordinate descent optimizer for classification problems exploiting convexity

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

Constrained Gaussian Processes: A Survey

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

A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges

LDRD Project Summary: Incorporating physical constraints into Gaussian process surrogate models

Constrained Gaussian Processes: A Survey

A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges

A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges

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

A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges

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

A Survey of Constrained Gaussian Process: Approaches and Implementation Challenges

A block coordinate descent optimizer for classification problems exploiting convexity

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

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

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

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