Our article “L. Sun, J.-X. Wang, Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data, Theor. App. Mech. Let. 10 (2020) 161-169 ” winning the 2021 Highest Citation Paper Award of Theoretical & Applied Mechanics Letters on January 2022. Congrats to Luning!
Pan Du won poster award @ Fall Symposium of Lucy Institute of Data Science and Society. Congrats! The Mayor of South Bend presented the award.
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
| 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 |
Xinyang is a recent graduate from XJTU (2019) and joined our group this spring. He will work on data assimilation for computational hemodynamics, welcome!