Journal Papers
Published
Penwarden, M., Zhe, S., Narayan, A., Kirby, R. M. Multifidelity Modeling for Physics-Informed Neural Networks (PINNs). Journal of Computational Physics, 451, 2022, 110844.
Penwarden, M., Zhe, S., Narayan, A., Kirby, R. M. A metalearning approach for Physics-Informed Neural Networks (PINNs): Application to parameterized PDEs. Journal of Computational Physics, 477, 2023, 111912.
Penwarden, M., Jagtap A.D., Zhe, S., Karniadakis, G.E., Kirby, R.M. A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. Journal of Computational Physics, 493, 2023, 112464.
Shukla, K.*, Oommen, V.*, Peyvan, A.*, Penwarden, M.*, Plewacki, N., Bravo, L., Ghoshal, A., Kirby, R.M., Karniadakis, G.E., 2023. Deep neural operators as accurate surrogates for shape optimization. Engineering Applications of Artificial Intelligence, 129, 2024, 107615.
Penwarden, M., Owhadi, H., & Kirby, R. M. (2024). Kolmogorov n-widths for multitask physics-informed machine learning (PIML) methods: Towards robust metrics. Neural Networks, 106703.
Submitted
Morrow, Zachary*, Michael Penwarden*, Brian Chen, Aurya Javeed, Akil Narayan, and John D. Jakeman. SUPN: Shallow Universal Polynomial Networks. arXiv preprint arXiv:2511.21414 (2025).
Chen, Wenqian, Yucheng Fu, Michael Penwarden, Pratanu Roy, and Panos Stinis. ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning. arXiv preprint arXiv:2602.11626 (2026).
* Indicates equal contribution
