4 papers · 1 filter
On the training of physics-informed neural operators for solving parametric partial differential equations
Nanxi Chen, Chuanjie Cui, Airong Chen +2
Physics-informed neural operators (PINOs) aim to learn solution operators for partial differential equations by using the governing physics as supervision, rather than relying sole…
GeoFunFlow: Geometric function flow matching for joint probabilistic inference of physical fields and complex geometries
Sifan Wang, Yajie Ji, Zhikai Wu +2
Inverse problems governed by partial differential equations (PDEs) arise widely in science and engineering, but are often ill-posed and limited by sparse, noisy observations. In ma…
FunDiff: Diffusion Models over Function Spaces for Physics-Informed Generative Modeling
Sifan Wang, Zehao Dou, Siming Shan +2
Recent advances in generative modeling -- particularly diffusion models and flow matching -- have achieved remarkable success in synthesizing discrete data such as images and video…
TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion
Zhikai Wu, Sifan Wang, Shiyang Zhang +5
Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynam…