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2020 – today
- 2024
[j6]Michael Penwarden:
Comment on "Trans-Net: A transferable pretrained neural networks based on temporal domain decomposition for solving partial differential equations" by D. Zhang, Y. Li, and S. Ying. Comput. Phys. Commun. 303: 109289 (2024)- [j5]Khemraj Shukla
, Vivek Oommen, Ahmad Peyvan, Michael Penwarden, Nicholas Plewacki, Luis Bravo, Anindya Ghoshal, Robert M. Kirby, George Em Karniadakis:
Deep neural operators as accurate surrogates for shape optimization. Eng. Appl. Artif. Intell. 129: 107615 (2024) - [j4]Michael Penwarden, Houman Owhadi, Robert M. Kirby:
Kolmogorov n-widths for multitask physics-informed machine learning (PIML) methods: Towards robust metrics. Neural Networks 180: 106703 (2024) - [i7]Michael Penwarden, Houman Owhadi, Robert M. Kirby:
Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust Metrics. CoRR abs/2402.11126 (2024) - 2023
- [j3]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
A metalearning approach for Physics-Informed Neural Networks (PINNs): Application to parameterized PDEs. J. Comput. Phys. 477: 111912 (2023) - [j2]Michael Penwarden, Ameya D. Jagtap, Shandian Zhe, George Em Karniadakis, Robert M. Kirby:
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. J. Comput. Phys. 493: 112464 (2023) - [c1]Shibo Li, Michael Penwarden, Yiming Xu, Conor Tillinghast, Akil Narayan, Mike Kirby, Shandian Zhe:
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. ICML 2023: 19855-19881 - [i6]Khemraj Shukla, Vivek Oommen, Ahmad Peyvan, Michael Penwarden, Luis Bravo, Anindya Ghoshal, Robert M. Kirby, George Em Karniadakis:
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils. CoRR abs/2302.00807 (2023) - [i5]Michael Penwarden, Ameya D. Jagtap, Shandian Zhe, George Em Karniadakis, Robert M. Kirby:
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. CoRR abs/2302.14227 (2023) - [i4]Haocheng Dai, Michael Penwarden, Robert M. Kirby, Sarang C. Joshi:
Neural Operator Learning for Ultrasound Tomography Inversion. CoRR abs/2304.03297 (2023) - 2022
- [j1]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
Multifidelity modeling for Physics-Informed Neural Networks (PINNs). J. Comput. Phys. 451: 110844 (2022) - [i3]Shibo Li, Michael Penwarden, Robert M. Kirby, Shandian Zhe:
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. CoRR abs/2210.12669 (2022) - 2021
- [i2]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs). CoRR abs/2106.13361 (2021) - [i1]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach. CoRR abs/2110.13361 (2021)
Coauthor Index
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