Jian-Xun Wang

Assistant Professor

University of Notre Dame

United States

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Jian-Xun Wang is an Assistant Professor at the University of Notre Dame in the United States. His research focuses on advanced computational methods in fluid dynamics, including neural differentiable modeling and uncertainty quantification. He has recently published articles on AI-powered simulations, reinforcement learning, and physics-informed neural networks.

Jian-Xun Wang

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Articles (26)

JAX-BTE: a GPU-accelerated differentiable solver for phonon Boltzmann transport equations

Implicit neural differentiable model for spatiotemporal dynamics

AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows

Asynchronous parallel reinforcement learning for optimizing propulsive performance in fin ray control

HybridNDiff-UQ: Uncertainty Quantification for Hybrid Neural Differentiable Modeling

CoNFiLD-inlet: Synthetic turbulence inflow using generative latent diffusion models with neural fields

Gradient-free optimization of non-differentiable hybrid neural solvers for spatially heterogeneous composites

Neural differentiable modeling with diffusion-based super-resolution for two-dimensional spatiotemporal turbulence

Physics-Informed Bayesian Neural Networks for Solving Phonon Boltzmann Transport Equation in Forward and Inverse Problems With Sparse and Noisy Data

Bayesian conditional diffusion models for versatile spatiotemporal turbulence generation

Probabilistic physics-integrated neural differentiable modeling for isothermal chemical vapor infiltration process

A neural network-based algorithm for the reconstruction and filtering of single particle trajectory in magnetic particle tracking

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics

Differentiable hybrid neural modeling for fluid-structure interaction

Conditional neural field latent diffusion model for generating spatiotemporal turbulence

Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks

Physics-integrated neural differentiable (PiNDiff) model for composites manufacturing

Effects of antagonistic muscle actuation on the bilaminar structure of ray-finned fish in propulsion

Deep learning-based surrogate model for three-dimensional patient-specific computational fluid dynamics

Physics-informed deep learning for solving phonon Boltzmann transport equation with large temperature non-equilibrium

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems

A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking

Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control

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

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

PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

Collaborators (11)

Tengfei Luo

Han Gao

Coen P. H. Elemans

Lian Duan

Qian Xue

Lu Lu

Liping Liu

XUDONG ZHENG

Zhihao Xu

Matthew Zahr

Huixuan Wu

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