3 citations · 3 across the 2 of their papers we have counts for
4 papers
CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
Xianglong Hou, Xinquan Huang, Paris Perdikaris
Neural operator surrogates for time-dependent partial differential equations (PDEs) conventionally employ autoregressive prediction schemes, which accumulate error over long rollou…
Physics-informed waveform inversion using pretrained wavefield neural operators
Xinquan Huang, Fu Wang, Tariq Alkhalifah
Full waveform inversion (FWI) is crucial for reconstructing high-resolution subsurface models, but it is often hindered, considering the limited data, by its null space resulting i…
PhysicsCorrect: A Training-Free Approach for Stable Neural PDE Simulations
Xinquan Huang, Paris Perdikaris
Neural networks have emerged as powerful surrogates for solving partial differential equations (PDEs), offering significant computational speedups over traditional methods. However…
Geological and Well prior assisted full waveform inversion using conditional diffusion models
Fu Wang, Xinquan Huang, Tariq Alkhalifah
Full waveform inversion (FWI) often faces challenges due to inadequate seismic observations, resulting in band-limited and geologically inaccurate inversion results. Incorporating…