FIDLE / Des neurones pour la physique, les physics-informed neural networks (PINNS)
Table of Contents
Tutorial: Understanding Physics-Informed Neural Networks (PINNs)
Video Title: FIDLE / Des neurones pour la physique, les physics-informed neural networks (PINNS)
Channel: CNRS - Formation FIDLE
Introduction:
In this tutorial, we will delve into the concept of Physics-Informed Neural Networks (PINNs) as introduced by Raissi et al. in 2019. PINNs are a powerful AI modeling technique that combines neural networks with physics principles to solve a wide range of problems in fluid dynamics, solid mechanics, and quantum mechanics.
Steps to Understand PINNs:
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Watch the Video: Start by watching the video to gain a comprehensive understanding of PINNs and their applications in solving forward and inverse problems involving nonlinear partial differential equations.
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Understand PINNs: PINNs leverage neural networks to simulate physical phenomena or predict experimental outcomes by incorporating input data such as initial conditions and physics parameters. They require a solid understanding of both the physics involved in a specific problem and AI modeling techniques.
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Key Concepts Covered:
- Introduction to PINNs
- Example of solving differential equations
- Balancing loss for improved accuracy
- Introduction to Fourier Neural Operators (FNO)
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Duration: The video is 2 hours long, providing an in-depth exploration of PINNs and related concepts.
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Further Learning: For those interested in deep learning, FIDLE offers a free Introduction to Deep Learning course. Visit FIDLE for more information.
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License: The video is licensed under Creative Commons CC BY-NC-ND 4.0, allowing for a summary overview of the content.
Conclusion:
By following these steps and delving into the video content, you will gain valuable insights into Physics-Informed Neural Networks (PINNs) and their applications in solving complex physics and engineering problems using AI techniques. Happy learning!