https://engineering.nd.edu/news/jian-xun-wang-receives-2021-nsf-career-award-for-data-augumented-cardiovascular-modeling/
Author: Jian-Xun Wang
New publication in PLoS One
Congrats to Pan Du on his first publication in PLoS One
- H. Wu, P. Du*, R. Kokate, J.-X. Wang, A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking, PLoS ONE, 16(7): e0254051, 2021. [Arxiv, DOI, bib]
![](https://sites.nd.edu/jianxun-wang/files/2021/07/image-2.png)
Two new publications in Physics of Fluids (editor’s Pick and featured articles)
- 1. H. Gao*, L. Sun, J.-X. Wang, Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels, Physics of Fluids, 33(7), 073603, 2021 (Editors’ Pick) [Arxiv, DOI, bib]
![](https://sites.nd.edu/jianxun-wang/files/2021/07/image.png)
- 2. A. Arzani, J.-X. Wang, R. D’Souza, Uncovering near-wall blood flow from sparse data with physics-informed neural networks, Physics of Fluids, 33, 071905, 2021 (Featured Article) [Arxiv, DOI, bib]
![](https://sites.nd.edu/jianxun-wang/files/2021/07/image-1.png)
Xinyang has passed PhD Qualify exam, Congrats
![](http://sites.nd.edu/jianxun-wang/files/2021/01/WechatIMG549-768x1024.jpeg)
Give a seminar talk at Intelligent and Bio-inspired Mechanics (IBiM) Seminar
Give a Seminar Talk at SUNY Buffalo, MAE Dept.
![](https://sites.nd.edu/jianxun-wang/files/2021/04/image-789x1024.png)
Our group will give 3 talks and a poster at SIAM CSE 2021
I will organize a mini-symposium entitled “Physics Informed Learning for Modeling and Discovery of Complex Systems” Parts I and II on 03/03/2021 at SIAM CSE. Moreover, our group will also give several talks at CSE21.
MS Talk: Wang et al. Physics-Informed Discretization-Based Learning: a Unified Framework for Solving PDE-Constrained Forward and Inverse Problem (2:15-2:30 CST, 03/03/2021) https://meetings.siam.org/sess/dsp_talk.cfm?p=108358
MS Talk: Han et al. Suppreresolution and Denoising of Flow Imaging using Physics-Constrained Discrete Learning (4:35-4:50 CST, 03/01/2021) https://meetings.siam.org/sess/dsp_talk.cfm?p=108020
MS Talk: Sun et al. System Identification by Sparse Bayesian Learinng (5:35-5:50 CST, 03/04/2021) https://meetings.siam.org/sess/dsp_talk.cfm?p=108437
Poster: Pan et al. Patient-Specific CFD Modeling of Aortic Dissection Augmented with 4D Flow MRI https://meetings.siam.org/sess/dsp_talk.cfm?p=110813
New publication in Computational Mechanics
H. Gao*, J.-X. Wang, A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems, Computational Mechanics
![](https://sites.nd.edu/jianxun-wang/files/2021/02/Screen-Shot-2021-02-26-at-11.18.49-1.png)
Mathematical modeling and simulation of complex physical systems based on partial differential equations (PDEs) have been widely used in engineering and industrial applications. To enable reliable predictions, it is crucial yet challenging to calibrate the model by inferring unknown parameters/fields (e.g., boundary conditions, mechanical properties, and operating parameters) from sparse and noisy measurements, which is known as a PDE-constrained inverse problem. In this work, we develop a novel bi-fidelity (BF) ensemble Kalman inversion method to tackle this challenge, leveraging the accuracy of high-fidelity models and the efficiency of low-fidelity models. The core concept is to build a BF model with a limited number of high-fidelity samples for efficient forward propagations in the iterative ensemble Kalman inversion. Compared to existing inversion techniques, salient features of the proposed methods can be summarized as follow: (1) achieving the accuracy of high-fidelity models but at the cost of low-fidelity models, (2) being robust and derivative-free, and (3) being code non-intrusive, enabling ease of deployment for different applications. The proposed method has been assessed by three inverse problems that are relevant to fluid dynamics, including both parameter estimation and field inversion. The numerical results demonstrate the excellent performance of the proposed BF ensemble Kalman inversion approach, which drastically outperforms the standard Kalman inversion in terms of efficiency and accuracy.
New Publication in JCP: Physics-Informed Geometry-Adaptive Convolutional Neural Networks
- H. Gao*, L. Sun, J.-X. Wang, PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain. Journal of Computational Physics, 428, 110079, 2021 [Arxiv, DOI, bib]
![](https://sites.nd.edu/jianxun-wang/files/2021/01/image.png)
Recently, the advent of deep learning has spurred interest in the development of physics-informed neural networks (PINN) for efficiently solving partial differential equations (PDEs), particularly in a parametric setting. Among all different classes of deep neural networks, the convolutional neural network (CNN) has attracted increasing attention in the scientific machine learning community, since the parameter-sharing feature in CNN enables efficient learning for problems with large-scale spatiotemporal fields. However, one of the biggest challenges is that CNN only can handle regular geometries with image-like format (i.e., rectangular domains with uniform grids). In this paper, we propose a novel physics-constrained CNN learning architecture, aiming to learn solutions of parametric PDEs on irregular domains without any labeled data. In order to leverage powerful classic CNN backbones, elliptic coordinate mapping is introduced to enable coordinate transforms between the irregular physical domain and regular reference domain. The proposed method has been assessed by solving a number of steady-state PDEs on irregular domains, including heat equations, Navier-Stokes equations, and Poisson equations with parameterized boundary conditions, varying geometries, and spatially-varying source fields. Moreover, the proposed method has also been compared against the state-of-the-art PINN with fully-connected neural network (FC-NN) formulation. The numerical results demonstrate the effectiveness of the proposed approach and exhibit notable superiority over the FC-NN based PINN in terms of efficiency and accuracy.
Our group will give 4 talks at 73rd Annual Meeting of APS Division of Fluid Dynamics
Please check out our presentations at APS DFD: R01.00011: Super-resolution and Denoising of Fluid Flows Using Physics-informed Convolutional Neural Networks Jian-Xun Wang, Han Gao, Luning Sun R01.00038: A unifying framework of solving forward and inverse problems in fluid mechanics via deep learning Han Gao, Jian-Xun Wang R10.00002 Physics-constrained multi-fidelity convolutional neural networks for surrogate fluid modeling Luning Sun, Han Gao, Jian-Xun Wang W07.00012 Computational simulation of aortic dissection with a comparison with 4D flow MRI Pan Du, Nicholas Burris, Julio Sotelo, Jian-Xun Wang http://meetings.aps.org/Meeting/DFD20/Content/3927 |