most citedPSP-GEN: Stochastic inversion of the Process-Structure-Property chain in materials design through deep, generative probabilistic modeling

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cs.LG2025

A unified physics-informed generative operator framework for general inverse problems

Gang Bao, Yaohua Zang

Solving inverse problems governed by partial differential equations (PDEs) is central to science and engineering, yet remains challenging when measurements are sparse, noisy, or wh…

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…