2 citations · 3 across the 18 of their papers we have counts for
4 papers · 1 filter
Learning Hidden Physics and System Parameters with Deep Operator Networks
Dibakar Roy Sarkar, Vijay Kag, Birupaksha Pal +1
Discovering hidden physical laws and identifying governing system parameters from sparse observations are central challenges in computational science and engineering. Existing data…
Basis-to-Basis Operator Learning Using Function Encoders
Tyler Ingebrand, Adam J. Thorpe, Somdatta Goswami +2
We present Basis-to-Basis (B2B) operator learning, a novel approach for learning operators on Hilbert spaces of functions based on the foundational ideas of function encoders. We d…
Causality-Respecting Adaptive Refinement for PINNs: Enabling Precise Interface Evolution in Phase Field Modeling
Wei Wang, Tang Paai Wong, Haihui Ruan +1
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving physical systems described by partial differential equations (PDEs). However, their accuracy in…
Efficient Training of Deep Neural Operator Networks via Randomized Sampling
Sharmila Karumuri, Lori Graham-Brady, Somdatta Goswami
Neural operators (NOs) employ deep neural networks to learn mappings between infinite-dimensional function spaces. Deep operator network (DeepONet), a popular NO architecture, has…