activity
20162022
most citedPhysics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data

15 citations · 26 across the 6 of their papers we have counts for

collaborators

10 papers

physics.comp-ph20226 cited

Physics-Informed Deep Learning for Solving Phonon Boltzmann Transport Equation with Large Temperature Non-Equilibrium

Ruiyang Li, Jian-Xun Wang, Eungkyu Lee +1

Phonon Boltzmann transport equation (BTE) is a key tool for modeling multiscale phonon transport, which is critical to the thermal management of miniaturized integrated circuits, b…

cond-mat.mtrl-sci2021

Machine Learning-Assisted Exploration of Thermally Conductive Polymers Based on High-Throughput Molecular Dynamics Simulations

Ruimin Ma, Hanfeng Zhang, Jiaxin Xu +4

Finding amorphous polymers with higher thermal conductivity is important, as they are ubiquitous in heat transfer applications. With recent progress in material informatics, machin…

physics.flu-dyn2021

Uncovering near-wall blood flow from sparse data with physics-informed neural networks

Amirhossein Arzani, Jian-Xun Wang, Roshan M. D'Souza

Near-wall blood flow and wall shear stress (WSS) regulate major forms of cardiovascular disease, yet they are challenging to quantify with high fidelity. Patient-specific computati…

physics.flu-dyn2020

Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels

Han Gao, Luning Sun, Jian-Xun Wang

High-resolution (HR) information of fluid flows, although preferable, is usually less accessible due to limited computational or experimental resources. In many cases, fluid data a…

physics.comp-ph202015 cited

Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data

Luning Sun, Jian-Xun Wang

In many applications, flow measurements are usually sparse and possibly noisy. The reconstruction of a high-resolution flow field from limited and imperfect flow information is sig…

math.NA2019

Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning

Han Gao, Jian-Xun Wang, Matthew J. Zahr

Projection-based model reduction has become a popular approach to reduce the cost associated with integrating large-scale dynamical systems so they can be used in many-query settin…