清华主页 EN
导航菜单

Physics-Informed Neural Networks for Scientific Computations: Algorithms and Applications

来源: 10-08

时间:13 October 2022 (Thursday) 9:00 a.m.

地点:BIMSA 1129B Zoom: 537 192 5549 PW: BIMSA

主讲人:Ameya D. Jagtap


返回顶部
相关文章
  • Learning constitutive models with neural networks

    AbstractIn this talk, I will introduce some work of learning constitutive equations in fluid mechanics and geophysics based on machine learningSpeaker Intro熊繁升,现任北京雁栖湖应用数学研究院助理研究员,曾任北京应用物理与计算数学研究所所聘博士后。先后毕业于中国地质大学(北京)、清华大学,美国耶鲁大学联合培养博士。研究兴趣主要集中于基于机器学习算法(DNN、PINN、DeepONet等)求解微分方程模型正/反问题...

  • [BIMSA-Tsinghua Seminar on Machine Learning and Differential Equations] From Neural PDEs to Neural Operators: Blending data and physics for fast predictions

    Abstract:We will review physics-informed neural network and summarize available extensions for applications in computational mechanics and beyond. We will also introduce new NNs that learn functionals and nonlinear operators from functions and corresponding responses for system identification. The universal approximation theorem of operators is suggestive of the potential of NNs in learning fro...