Prof. Jian-xun Wang's research group -- we advance knowledge at the Interface of scientific AI and computational physics (scientific machine learning, data assimilation, physics-informed deep learning, Bayesian learning, differentiable programming, uncertainty quantification)
We are welcoming submission of original research to an article collection entitled “Physics-informed Machine Learning and Its Real-world Applications” of Scientific Reports (Springer Nature). As Guest Editor for the Collection, I hope you will consider this excellent outlet for your research in this field.
This article collection is a great opportunity to highlight this important area, and we hope you will be able to contribute. Please don’t hesitate to contact me if you have any questions. The deadline is Nov. 30, 2022. For more details, please see https://www.nature.com/collections/hdjhcifhad/guest-editors
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.
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
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.
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!
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
