9 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…
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…
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…
Generative Reconstruction of Spatiotemporal Wall-Pressure in Turbulent Boundary Layers via Patchwise Latent Diffusion
Xiantao Fan, Meet Hemant Parikh, Yi Liu +4
Wall-pressure fluctuations in turbulent boundary layers drive flow-induced noise, structural vibration, and hydroacoustic disturbances, especially in underwater and aerospace syste…
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…