Happy to be selected to the list of 2022 Top 10 Outstanding Chinese American Youth, during Asian American and Pacific Islander Heritage Month.
Lastest News
Invited Talk at USACM UQ Virtual Seminar Series
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
Here is the talk recording
Will host a MS a SIAM UQ22 and our group will give 5 presentations
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.
- 3:00 – 3:25 PM, 04/12/2022, (Chastain J – 6th Floor), MS26, Xinyang Liu et al. Physics-Informed Model-Based Reinforcement Learning with Quantified Uncertainties.
- 3:15 – 3:40 PM, 04/13/2022, (Peachtree 2 – 8th Floor), MS71, Luning Sun et al. Deep Bayesian Spline Learning for Closed-Form Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty
- 3:15 – 3:40 PM, 04/13/2022, (Peachree A 8th Floor), MS72, Han Gao et al. A long-span geometric deep learning framework based on attention mechanism for fast surrogate predictions of spatiotemporal dynamics
- 8:40 – 9:05 AM, 04/14/2022, (Augusta E – 7th Floor), MS89, Xinyang Liu et al. PDE-preserved network architecture for predicting spatiotemporal dynamics
- 5:30 – 5:55 PM, 04/14/2022, (Chastain E – 6th Floor), MS123, Pan Du et al. Fast Surrogate of 3-D Patient-Specific Computational Fluid Dynamics Using Statistical Shape Modeling and Deep Learning
Our paper has been accepted by ICLR 2022
Happy to share our work “Predicting Physics in Mesh-Reduced Space with Temporal Attention”, which has been accepted to ICLR 2022. This is a collaborative work with Tufts and DeepMind. Particularly congrats to the student authors X. Han and H. Gao. Arxiv link: https://lnkd.in/dxvZRXWC.
Highest Citation Paper Award
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 give a seminar talk @ AI institute at Renmin University of China
Give a seminar talk @ ANL
Pan Du won poster award @ Fall Symposium of Lucy Institute of Data Science and Society. Congrats!
Pan Du won poster award @ Fall Symposium of Lucy Institute of Data Science and Society. Congrats! The Mayor of South Bend presented the award.
Will host a MS at USNCCM16 and our group will give 3 presentations
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.
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.; 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 abstract 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 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 PDEsabstract 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.