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20082026
most citedPhysics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

1.1k citations · 1.4k across the 43 of their papers we have counts for

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cs.LG2025

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling

Yaohua Zang, Phaedon-Stelios Koutsourelakis

Solving parametric partial differential equations (PDEs) and associated PDE-based, inverse problems is a central task in engineering and physics, yet existing neural operator metho…

cs.LG2020★ 1 cited

Embedded-physics machine learning for coarse-graining and collective variable discovery without data

Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis

We present a novel learning framework that consistently embeds underlying physics while bypassing a significant drawback of most modern, data-driven coarse-grained approaches in th…