Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach. - Michael Penwarden; Shandian Zhe; Akil Narayan 001; Robert M. Kirby (CoRR, 2021)

Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. - Shibo Li; Michael Penwarden; Robert M. Kirby; Shandian Zhe (CoRR, 2022)

Multifidelity modeling for Physics-Informed Neural Networks (PINNs). - Michael Penwarden; Shandian Zhe; Akil Narayan 001; Robert M. Kirby (J. Comput. Phys., 2022)

Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. - Shibo Li; Michael Penwarden; Yiming Xu; Conor Tillinghast; Akil Narayan 001; Mike Kirby; Shandian Zhe (ICML, 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)

Neural Operator Learning for Ultrasound Tomography Inversion. - Haocheng Dai; Michael Penwarden; Robert M. Kirby; Sarang C. Joshi (CoRR, 2023)

A metalearning approach for Physics-Informed Neural Networks (PINNs): Application to parameterized PDEs. - Michael Penwarden; Shandian Zhe; Akil Narayan 001; Robert M. Kirby (J. Comput. Phys., 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., 2023)

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. - Michael Penwarden (Comput. Phys. Commun., 2024)

Deep neural operators as accurate surrogates for shape optimization. - Khemraj Shukla; Vivek Oommen; Ahmad Peyvan; Michael Penwarden; Nicholas Plewacki; Luis Bravo; Anindya Ghoshal; Robert M. Kirby; George Em Karniadakis (Eng. Appl. Artif. Intell., 2024)

Kolmogorov n-widths for multitask physics-informed machine learning (PIML) methods: Towards robust metrics. - Michael Penwarden; Houman Owhadi; Robert M. Kirby (Neural Networks, 2024)