Xinquan Huang

Xinquan Huang

University of Pennsylvania

💬 Invited Talks

  • 2026.02, Bridging AI and Physics for Scientific Discovery: Toward Intelligent Modeling and Inference of Physical Systems, School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen
  • 2026.01, From Seismic Waves to Subsurface Dynamics: Toward Intelligent Imaging and Monitoring of the Earth, Department of Earth and Space Sciences, Southern University of Science and Technology
  • 2026.01, Bridging AI and Physics for Scientific Discovery: From Neural PDE Solvers to Generative Inversion, Institute of Automation, Chinese Academy of Sciences
Earlier talks
  • 2025.12, Bridging AI and Physics for Scientific Discovery: From Neural PDE Solvers to Generative Inversion, Guanghua Young Scholars Forum, Fudan University
  • 2025.12, Bridging AI and Physics for Scientific Discovery: From Neural PDE Solvers to Generative Inversion, Elite Talent Forum, Shanghai Innovation Institute
  • 2025.12, Bridging AI and Physics for Scientific Discovery: From Neural PDE Solvers to Generative Inversion, Mozi Forum, University of Science and Technology of China
  • 2025.11, Bridging AI and Physics for Scientific Discovery: From Neural PDE Solvers to Generative Inversion, Yongriver Forum, Eastern Institute of Technology, China
  • 2025.09, Learning to Solve PDEs with Physics-Informed machine learning, SLB (Schlumberger), USA
  • 2025.02, Physics-informed Machine Learning: neural PDE Solvers and their Applications in Subsurface Monitoring, Peking University Intelligent Youth Forum, Peking University, China
  • 2025.02, Physics-informed Machine Learning: neural PDE Solvers and their Applications in Subsurface Monitoring, The Youth Forum of Beijing Zhongguancun Academy, China
  • 2024.03, Physics-informed Machine Learning: neural PDE Solvers and their Applications in Subsurface Monitoring, Earth and Energy Division, Lawrence Livermore National Laboratory, USA
  • 2023.10, Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition, Purdue University, USA
  • 2021.06, Physics-informed neural network for high-frequency wavefield representation, KAUST Virtual Workshop Intelligent illumination of the Earth