Follow
Michael Penwarden
Michael Penwarden
PhD Candidate, University of Utah
Verified email at sci.utah.edu - Homepage
Title
Cited by
Cited by
Year
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions
M Penwarden, AD Jagtap, S Zhe, GE Karniadakis, RM Kirby
Journal of Computational Physics 493, 112464, 2023
402023
A metalearning approach for physics-informed neural networks (PINNs): Application to parameterized PDEs
M Penwarden, S Zhe, A Narayan, RM Kirby
Journal of Computational Physics 477, 111912, 2023
30*2023
Multifidelity modeling for physics-informed neural networks (pinns)
M Penwarden, S Zhe, A Narayan, RM Kirby
Journal of Computational Physics 451, 110844, 2022
302022
Deep neural operators as accurate surrogates for shape optimization
K Shukla, V Oommen, A Peyvan, M Penwarden, N Plewacki, L Bravo, ...
Engineering Applications of Artificial Intelligence 129, 107615, 2024
20*2024
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
S Li, M Penwarden, Y Xu, C Tillinghast, A Narayan, RM Kirby, S Zhe
40th International Conference on Machine Learning 202, 19855-19881, 2023
42023
Neural Operator Learning for Ultrasound Tomography Inversion
H Dai, M Penwarden, RM Kirby, S Joshi
Medical Imaging with Deep Learning (short paper), 2023
12023
Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust Metrics
M Penwarden, H Owhadi, RM Kirby
arXiv preprint arXiv:2402.11126, 2024
2024
The system can't perform the operation now. Try again later.
Articles 1–7