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Invited Talk at USACM UQ Virtual Seminar Series

Posted on by Jian-Xun Wang

I will give invited talk at #USACM#UQ Virtual Seminar Organized by USACM UQ TTA. (Time: 15:00 PM-16:00 PM (EST US), 5/12/2022. Zoom Link: listed below). The topic is about “how to leverage physics in ML for computational mechanics — Physics-informed, PDE-structure preserved Learning for problems with complex geometries”. I am happy to have Prof. Michael Brenner be my discussant sharing his great insights in the field of scientific machine learning #SciML. Also thank the organization committee: Abani Patra, Serge Prudhomme, Johann Guilleminot.

In this talk, I will discuss our recent developments of on SciML from several different aspects, including (1) how to leverage physics to inspire network architecture design (PDE-preserved deep learning #PPNN), (2) use physics to inform network training #PINN, (3) physics-informed geometric deep learning (#GeometricDL) for complex geometries and irregular domains. This work is supported National Science Foundation (NSF)#OAC#CMMI, Aerospace & Mechanical Engineering at Notre Dame, Center for Sustainable Energy at Notre Dame, ND-ECI, ND Lucy Institute of Data Science.

Welcome to join us at https://lnkd.in/gH6AHp3V. See you on Thursday

US Association for Computational Mechanics (USACM), TTA: Uncertainty Quantification and Probabilistic Modeling.

Here is the talk recording

Will host a MS a SIAM UQ22 and our group will give 5 presentations

Posted on by Jian-Xun Wang

Together with Yeonjong Shin (Brown University) and Xueyu Zhu (U Iowa), we will co-host a 3-part mini-sympoiusm entitled “Recent Advances in Machine Learning and Data-Driven Methods for Physical Sciences and Engineering”, where 12 speakers will give talks on scientific machine learning and data-driven modeling.

  • 9:30 AM – 11:30 AM, 04/12/2022, (Augusta E – 7th Floor) MS2 Part I
  • 8:10 AM – 10:10 AM, 04/13/2022, (Augusta E – 7th Flow) MS45 Part II
  • 8:10 AM – 10:10 AM, 04/14/2022, (Augusta E – 7th Flow) MS89 Part III

Our group will give 5 talks at SIAM UQ22.

Highest Citation Paper Award

Posted on by Jian-Xun Wang

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!

Will host a MS at USNCCM16 and our group will give 3 presentations

Posted on by Jian-Xun Wang

Together with Danial, Kathryn, Alireza, and Hao, we will co-host a mini-sympoisum entiled Physics-Informed Learning and Data-Enabled Predictive Modeling and Discovery of Complex Systems, where 15 speakers will give excellent talks on scientific machine learning and data-driven predictive modeling. (10:00-18:00 (UTC), 07/26/2021). Please check out.

Our group will also give 3 talks at USNCCM:

  • 14:00-14:20 (UTC) 07/27/2021: X.-Y. Liu* and J.-X. Wang, Physics-informed model-based deep reinforcement learning for dynamic control.
  • 17:20-17:40 (UTC) 07/27/2021: P. Du*, X. Zhu, J.-X. Wang, Surrogate modeling for 3-D patient-specific hemodynamics using statistical shape modeling and deep learning
  • 15:00-15:20 (UTC) 07/27/2021: J.-X. Wang*, H. Gao, L. Sun, Physics-informed discretization-based learning: a unified framework for solving PDE-constrained forward and inverse problems

 

Will host a mini-symposium at SIAM AN 21 (July 19-23), and Xinyang will give a presentation on MBRL for control.

Posted on by Jian-Xun Wang

Together with Prof. Huan Xun@University of Michigan, we will host a two-section mini-symposium (MS48) entiled: Physics-aware machine learning for solving and discovering PDEs, part I (MS48) and part II (MS105)

Part I (MS 48), Tuesday, July 20

  • 4:30-4:55 Deep Neural Network Modeling of Unknown PDEs in Nodal Space abstract Zhen Chen, Ohio State University, U.S.; updated Victor Churchill, Dartmouth College, U.S.; Kailiang Wu and Dongbin Xiu, Ohio State University, U.S. 
  • 5:00-5:25 Deep Learning Methods for Discovering Physics from Data abstract Joseph Bakarji, Jared L. Callaham, and Kathleen Champion, University of Washington, U.S.; J. Nathan Kutz, University of Washington, Seattle, U.S.; Steve Brunton, University of Washington, U.S.
  • 5:30-5:55 Data-Driven Learning of Nonlocal Models:from High-Fidelity Simulations to Constitutive Laws abstract Yue Yu and Huaiqian You, Lehigh University, U.S.; Stewart Silling and Marta D’Elia, Sandia National Laboratories, U.S. 
  • 6:00-6:25 Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control abstractupdated Xinyang Liu and Jianxun Wang, University of Notre Dame, U.S.

Part II (MS 105), Friday, July 23

  • 3:30-3:55 Optimal Experimental Design for Variational System Identification of Material Physics Phenomena abstract Wanggang Shen, Zhenlin Wang, Krishna Garikipati, and updated Xun Huan, University of Michigan, U.S. 
  • 4:00-4:25 Learning Stochastic Closures Using Sparsity-Promoting Ensemble Kalman InversionabstractJinlong Wu, Tapio Schneider, and Andrew Stuart, California Institute of Technology, U.S. 
  • 4:30-4:55 PhyCRNet: Physics-Informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEsabstractupdated Pu Ren and Chengping Rao, Northeastern University, U.S.; Jianxun Wang, University of Notre Dame, U.S.; Yang Liu and Hao Sun, Northeastern University, U.S. 
  • 5:00-5:25 Practical Uncertainty Quantification for Learning Partial Differential Equations from Data with Deep EnsemblesabstractSteven Atkinson and Panagiotis Tsilifis, GE Global Research, U.S.