Category: News

Will give 7 presentations @APS DFD 24

Posted on by Jian-Xun Wang

We will be giving 7 talks in APS DFD meeting (from Sunday Nov 24 to Tuesday Nov 26 @Salt Lake City) https://meetings.aps.org/Meeting/DFD24/PersonIndex/4610. If you attend the meeting, please check them out.

  1. J.-X. Wang et al. CoNFiLD: Conditional Neural Field Latent Diffusion Model Generating Spatiotemporal Turbulence (10:50 AM, Sunday, November 24, 2024 Room: 255 E)
  2. P. Du et al. A Multi-Modal Implicit Neural Representation Method for Dimension Reduction of Spatiotemporal Flow Data (8:25 AM–10:40 AM, Monday, November 25, 2024
    Room: 155 B)
  3. X.-Y. Liu et al. Diff-FlowFSI: A GPU-accelerated, JAX-based Differentiable CFD Solver for Turbulent Flow and Fluid-Structure Interactions (9:05 AM–9:18 AM, Monday, November 25, 2024
    Room: 155 B)
  4. L. Sun et al. Multi-fidelity Reinforcement Learning Control for Complex Dynamical Systems (5:24 PM–5:37 PM)
  5. X. Fan et al. Generative AI for Synthesizing Spatio-temporal Wall Pressure Fluctuations in Turbulent Boundary Layers (1:03 PM–1:16 PM, Tuesday, November 26, 2024)
  6. Y. Liu et al. A DNS database of wall-pressure fluctuations beneath turbulent boundary layers with pressure gradients (1:29 PM–1:42 PM, Tuesday, November 26, 2024)
  7. M. H. Parikh, Generative Latent Diffusion Model for Stochastic Inflow Turbulence Synthesis (1:16 PM–1:29 PM, Tuesday, November 26, 2024)

Our lab received research grant from Lucy Institute

Posted on by Jian-Xun Wang

https://lucyinstitute.nd.edu/news-events/news/lucy-family-institute-for-data-society-announces-awards-for-transformative-proposals/

We got one-year research grant from Lucy Institute of Data Science and Society.

Jian-Xun Wang, Assistant Professor in the Department of Aerospace and Mechanical Engineering (AME), and Chaoli Wang, Professor in CSE, were awarded this grant to develop a novel Bayesian deep learning framework for automating vessel segmentation from medical images to enable rapid construction of personalized, patient-specific computational models.

Thank Lucy Institute!

Welcome submissions to article collection of “Physics-informed Machine Learning”

Posted on by Jian-Xun Wang

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

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