ETH Zürich DLSC: Physics-Informed Neural Networks - Applications [VOtLUk-wmk2]
ETH Zürich DLSC: Physics-Informed Neural Networks - Applications [VOtLUk-wmk2]
| 1h 28m 28s | Video has closed captioning.
↓↓↓ LECTURE OVERVIEW BELOW ↓↓↓ ETH Zürich Deep Learning in Scientific Computing 2023 Lecture 5: Physics-Informed Neural Networks - Applications Course Website (links to slides and tutorials): Lecturers: Ben Moseley and Siddhartha Mishra ▬ Lecture Content ▬▬▬▬▬▬▬▬▬ 0:00 - Lecture overview 1:45 - What is a physics-informed neural network (PINN)? 11:59 - PINNs as a general framework 17:17 - PINNs for solving the Burgers' equation 20:20 - How to train PINNs 28:34 - 🔴 Live coding a PINN - part 1 | Code: github.com/benmoseley/DLSC-2023 39:42 - Training considerations 44:27 - [break - please skip] 53:07 - Simulation with PINNs 1:00:14 - Solving inverse problems with PINNs 1:14:00 - 🔴 Live coding a PINN - part 2 | Code: 1:24:10 - Equation discovery with PINNs ▬ Course Overview ▬▬▬▬▬▬▬▬▬ Lecture 1: Course Introduction Lecture 2: Introduction to Deep Learning Part 1 Lecture 3: Introduction to Deep Learning Part 2 Lecture 4: Physics-Informed Neural Networks - Introduction Lecture 5: Physics-Informed Neural Networks - Applications Lecture 6: Physics-Informed Neural Networks - Limitations and Extensions Lecture 7: Introduction to Operator Learning Part 1 Lecture 8: Introduction to Operator Learning Part 2 Lecture 9: Deep Operator Networks Lecture 10: Neural Operators Lecture 11: Fourier Neural Operators and Convolutional Neural Operators Lecture 12: Introduction to Differentiable Physics Part 1 Lecture 13: Introduction to Differentiable Physics Part 2 ▬ Course Learning Objectives ▬▬▬▬▬ The objective of this course is to introduce students to advanced applications of deep learning in scientific computing. The focus will be on the design and implementation of algorithms as well as on the underlying theory that guarantees reliability of the algorithms. We provide several examples of applications in science and engineering where deep learning based algorithms outperform state of the art methods. By the end of the course you should be: - Aware of advanced applications of deep learning in scientific computing - Familiar with the design, implementation and theory of these algorithms - Understand the pros/cons of using deep learning - Understand key scientific machine learning concepts and themes #crypto training #bitcoin-transaktion verfolgen #bitcoin sats
Aired: December 16, 2024
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