4 citations · 5 across the 5 of their papers we have counts for
5 papers
CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent Diffusion Models with Neural Fields
Xin-Yang Liu, Meet Hemant Parikh, Xiantao Fan +4
Eddy-resolving turbulence simulations require stochastic inflow conditions that accurately replicate the complex, multi-scale structures of turbulence. Traditional recycling-based…
Neural Differentiable Modeling with Diffusion-Based Super-resolution for Two-Dimensional Spatiotemporal Turbulence
Xiantao Fan, Deepak Akhare, Jian-Xun Wang
Simulating spatiotemporal turbulence with high fidelity remains a cornerstone challenge in computational fluid dynamics (CFD) due to its intricate multiscale nature and prohibitive…
CoNFiLD: Conditional Neural Field Latent Diffusion Model Generating Spatiotemporal Turbulence
Pan Du, Meet Hemant Parikh, Xiantao Fan +2
This study introduces the Conditional Neural Field Latent Diffusion (CoNFiLD) model, a novel generative learning framework designed for rapid simulation of intricate spatiotemporal…
Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation
Han Gao, Xu Han, Xiantao Fan +4
Turbulent flows have historically presented formidable challenges to predictive computational modeling. Traditional numerical simulations often require vast computational resources…
Differentiable hybrid neural modeling for fluid-structure interaction
Xiantao Fan, Jian-Xun Wang
Solving complex fluid-structure interaction (FSI) problems, which are described by nonlinear partial differential equations, is crucial in various scientific and engineering applic…