ETH Zürich AISE 2025: Lecture 3 Physics-Informed Neural Networks – Introduction скачать в хорошем качестве

ETH Zürich AISE 2025: Lecture 3 Physics-Informed Neural Networks – Introduction

↓↓↓ LECTURE OVERVIEW BELOW ↓↓↓ ETH Zürich AI in the Sciences and Engineering Lecture Series 2025 Course Website (links to slides and tutorials): https://camlab-ethz.github.io/ai4s-co... Complete Lecture Series as Playlist :    • ETH Zürich | AI in the Sciences and Engine...   Lecturers : Prof. Siddhartha Mishra and David Graber ▬ Course Description ▬▬▬ AI is having a profound impact on science by accelerating discoveries across physics, chemistry, biology, and engineering. This course aims to present a highly topical selection of state-of-the-art AI applications across these fields. Emphasis will be placed on using AI, particularly deep learning, to understand physical and engineering systems, mathematically modelled by PDEs. Topics include physics modeled by PDEs and the limitations of traditional simulators; neural PDE solvers (PINNs and variants); neural operators (FNO, CNO, Operator Transformers); graph neural networks and flexible Transformer frameworks for complex geometries; generative AI (diffusion, flow) for multiscale problems and uncertainty quantification; physics foundation models; downstream applications such as uncertainty quantification, inverse problems, design, and AI for weather and climate; and selected examples in chemistry and biology (GNNs and generative AI for structure-based drug design). By the end of the course, students will be familiar with advanced applications, core algorithmic design and theory, the trade-offs of using AI for scientific and engineering problems, and key scientific machine learning themes. ▬ Course Overview ▬▬▬ Lecture 1: Course Introduction    • ETH Zürich AISE 2025: Lecture 1 Course Int...   Lecture 2: Introduction to Deep Learning    • ETH Zürich AISE 2025: Lecture 2 Introducti...   Lecture 3: Physics-Informed Neural Networks – Introduction    • ETH Zürich AISE 2025: Lecture 3 Physics-In...   Lecture 4: PINNs - Theoretical Insights    • ETH Zürich AISE 2025: Lecture 4 PINNs - Th...   Lecture 5: Operator Learning - Introduction    • ETH Zürich AISE 2025: Lecture 5 Operator L...   Lecture 6: Operator Learning - FNO    • ETH Zürich AISE 2025: Lecture 6 Operator L...   Lecture 7: Operator Learning - ReNO    • ETH Zürich AISE 2025: Lecture 7 Operator L...   Lecture 8: Operator Learning - Operator Transformer    • ETH Zürich AISE 2025: Lecture 8 Operator L...   Lecture 9: Operator Learning - Graph-based Models    • ETH Zürich AISE 2025: Lecture 9 Operator L...   Lecture 10: Operator Learning - GAOT    • ETH Zürich AISE 2025: Lecture 10 Operator ...   Lecture 11: Generative Models for PDEs - GenCFD    • ETH Zürich AISE 2025: Lecture 11 Generativ...   Lecture 12: Foundation Models for PDEs - Poseidon    • ETH Zürich AISE 2025: Lecture 12 Foundatio...   Lecture 13: AI for Chemistry and Biology - Part I    • ETH Zürich AISE 2025: Lecture 13 AI in Che...   Lecture 14: AI for Chemistry and Biology - Part II    • ETH Zürich AISE 2025: Lecture 14: AI in Ch...