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발표자: 김한결 석사과정 ([email protected]) [Seminar Overview] Physics-Informed Neural Network (PINN)의 소개와 기본 개념 및 구현 방법 PINN의 몇가지 특징과 DeepONet Application 사례: inverse design of metamaterials Challenges in PINN: gradient pathology, loss imbalance, spectral bias, etc. [포함 논문 List] Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations (Journal of Computational Physics 2019) DeepXDE: A Deep Learning Library for Solving Differential Equations (Journal of Computational Physics 2019) Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators (Nature machine intelligence 2021) Physics-informed neural networks for inverse problems in nano-optics and metamaterials (Optics express 2020) Understanding and mitigating gradient flow pathologies in physics-informed neural networks (SIAM 2021) Characterizing possible failure modes in physics-informed neural networks (NeurIPS 2021) When and why PINNs fail to train: A neural tangent kernel perspective (Journal of Computational Physics 2021) Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems (Computer Methods in Applied Mechanics and Engineering 2022) RoPINN: Region Optimized Physics-Informed Neural Networks (NeurIPS 2024) [Chapter] 00:00 Intro 00:44 00 Overview 02:02 01 Background 12:13 02 PINN Basics 21:27 03 Implementation & Characteristics 29:13 04 Application 33:39 05 Challenges 46:21 06 Conclusion