[기계분야 인공지능 최신 연구 동향] Recent Trends on Physics-informed NN
2021년 11월 3일, 대한기계학회 본부학술대회 기계인공지능연구회 특별세션
한국과학기술원 이승철 교수
[물리지식기반 인공지능] Recent Advances in Physics-informed Machine Learning
2023년 5월 18일, 대한기계학회 CAE및응용역학 부문 춘계학술대회 특별세션
포항공대 수학과 최민석 교수
[물리지식기반 인공지능] Recent Trend in PINN and its Applications to NDT
2024년 1월 31일, 기계인공지능연구회 Scientific Machine Learning Workshop
광주과학기술원 오현석 교수
Topics Jupyter notebook Slides YouTube
Physics-informed Neural Networks (PINN) iNote#01 pdf#01 iYouTube#01
PINN as a PDE Solver iNote#02 pdf#02 iYouTube#02
PINN with Data (Fluid) iNote#03 pdf#03 iYouTube#03
PINN with Data (Solid) iNote#04 pdf#04 iYouTube#04
Challenges in PINN pdf#05 iYouTube#05
Deep Learning in Scientific Computing by Prof. Siddhartha Mishra at ETH Zurich
Physics Informed Machine Learning by Prof. Steve Brunton at the University of Washington
Intro. to PINN by Dr. Ameya Jagtap (Brown University)
PINN Implementation with PyTorch and JAX by Juan Toscano
Prof. George Karniadakis, From Neural PDEs to Neural Operators: Blending data and physics
Prof. Steve Brunton, Machine Learning for Scientific Discovery with Examples in Fluid Mechanics
Prof. Anima Anandkumar, EI 2023 Plenary 1: Neural Operators for Solving PDEs
Prof. Paris Perdikaris, When and why physics-informed neural networks fail to train
Prof. Ricardo Vinuesa, Physics-informed neural networks for fluid mechanics
Dr. Amir Gholaminejad, Rethinking Physics Informed Neural Networks [NeurIPS'21]
Towards Trustworthy Scientific Machine Learning: Theory, Algorithms, and Applications
Prof. Yeonjong Shin, the Department of Mathematics at NC State University
NVIDIA GTC: Using Physics-Informed Neural Networks and SimNet to Accelerate Product Development
Kinetic Vision