Michael Penwarden

According to our database, Michael Penwarden authored at least 9 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust Metrics.

Michael Penwarden, Houman Owhadi, Robert M. Kirby

CoRR, 2024

2023

A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions.

Michael Penwarden, Ameya D. Jagtap, Shandian Zhe, George Em Karniadakis, Robert M. Kirby

J. Comput. Phys., November, 2023

A metalearning approach for Physics-Informed Neural Networks (PINNs): Application to parameterized PDEs.

Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby

J. Comput. Phys., March, 2023

Neural Operator Learning for Ultrasound Tomography Inversion.

Haocheng Dai, Michael Penwarden, Robert M. Kirby, Sarang C. Joshi

CoRR, 2023

Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils.

Khemraj Shukla, Vivek Oommen, Ahmad Peyvan, Michael Penwarden, Luis Bravo, Anindya Ghoshal, Robert M. Kirby, George Em Karniadakis

CoRR, 2023

Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks.

Shibo Li, Michael Penwarden, Yiming Xu, Conor Tillinghast, Akil Narayan, Mike Kirby, Shandian Zhe

Proceedings of the International Conference on Machine Learning, 2023

2022

Multifidelity modeling for Physics-Informed Neural Networks (PINNs).

Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby

J. Comput. Phys., 2022

Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks.

Shibo Li, Michael Penwarden, Robert M. Kirby, Shandian Zhe

CoRR, 2022

2021

Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach.

Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby

CoRR, 2021