3 papers
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…
math-ph2025
Design-GenNO: A Physics-Informed Generative Model with Neural Operators for Inverse Microstructure Design
Yaohua Zang, Phaedon-Stelios Koutsourelakis
Inverse microstructure design plays a central role in materials discovery, yet remains challenging due to the complexity of structure-property linkages and the scarcity of labeled…
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…