100 citations · 184 across the 9 of their papers we have counts for
4 papers · 1 filter
ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning
Wenqian Chen, Zhi-Feng Wei, Yucheng Fu +3
Learning solution operators on arbitrary geometries remains a central challenge in scientific machine learning, especially for many-query simulation, physics-informed learning, and…
SUPN: Shallow Universal Polynomial Networks for Efficient Function Approximation
Zachary Morrow, Michael Penwarden, Brian Chen +3
Deep neural networks (DNNs) and Kolmogorov-Arnold networks (KANs) are flexible function approximators, but their accuracy can require substantial network depth, width, and trainabl…
Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust Metrics
Michael Penwarden, Houman Owhadi, Robert M. Kirby
Physics-informed machine learning (PIML) as a means of solving partial differential equations (PDE) has garnered much attention in the Computational Science and Engineering (CS&E)…
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
Shibo Li, Michael Penwarden, Yiming Xu +4
Physics-informed neural networks (PINNs) are emerging as popular mesh-free solvers for partial differential equations (PDEs). Recent extensions decompose the domain, apply differen…