1 citations · 1 across the 7 of their papers we have counts for
8 papers
Differentiable Hybrid Neural-CFD Modelling of Wall-Bounded Turbulence: Coupled Learning of Subgrid-Scale and Wall Closures
Xiantao Fan, Yi Liu, Meng Wang +1
Wall-modelled large-eddy simulation (WMLES) treats the subgrid-scale (SGS) closure, wall closure and numerical discretization as independent components, although their effects are…
JAX-AMG: A GPU-Accelerated Differentiable Sparse Linear Solver Library for JAX
Yi Liu, Xiantao Fan, Jian-Xun Wang
Sparse linear systems from PDE discretizations are central to scientific computing, yet no existing JAX-ecosystem solver simultaneously provides GPU-accelerated algebraic multigrid…
A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction
Xin-Yang Liu, Xiantao Fan, Jian-Xun Wang
Predicting complex spatiotemporal dynamics in physical processes often demands computationally expensive numerical methods or data-driven neural networks that suffer from high trai…
Offline Reinforcement Learning for Fluid Controls: Data-based Multi-observational Policy Extraction
Deepak Akhare, Luning Sun, Xin-Yang Liu +4
Active flow control is a fundamental application in engineering. Recent advances in deep reinforcement learning have made progress in this field. However, the classical online RL a…
Conditional neural field for spatial dimension reduction of turbulence data: a comparison study
Junyi Guo, Pan Du, Xiantao Fan +2
We investigate conditional neural fields (CNFs), mesh-agnostic, coordinate-based decoders conditioned on a low-dimensional latent, for spatial dimensionality reduction of turbulent…
Diff-FlowFSI: A GPU-Optimized Differentiable CFD Platform for High-Fidelity Turbulence and FSI Simulations
Xiantao Fan, Xinyang Liu, Meng Wang +1
Turbulent flows and fluid-structure interactions (FSI) are ubiquitous in scientific and engineering applications, but their accurate and efficient simulation remains a major challe…