Building a Physics-Informed Neural Network for realworld app
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Discover how to build physics-informed neural networks (PINNs) for real-world applications. Learn how to integrate domain-specific knowledge with deep learning to solve complex scientific and engineering problems more effectively.
At a Glance
In the age of AI, neural networks solve complex problems, such as Computer Vision (CV) and Large Language Model (LLM), but don’t forget the real world—physical principles. Integrating these physics principles into machine learning ensures precise understanding. What happens when we bring the power of networks together with the timeless principles of physics? The result is Physics-Informed Neural Networks (PINNs), an innovative approach that allows us to solve challenging differential equations with the precision of physics and the flexibility of machine learning.
A look at the project ahead
By completing this project, you will not only gain a deeper understanding of how AI can be informed by the physical world, but you will also acquire the skills to implement these powerful models in your own work, opening new possibilities in scientific research, engineering, and beyond.
What you’ll learn
- Understand the fundamental concepts behind Physics-Informed Neural Networks and how they integrate physical principles into machine learning models.
- Gain hands-on experience in building and training PINNs using PyTorch, and apply them to solve differential equations that model real-world phenomena.
What you’ll need
- Prerequisites: A basic understanding of neural networks and machine learning principles. Familiarity with differential equations and physical laws will be beneficial.
- Technology requirements: This project will be conducted using Python, with a focus on the PyTorch library. We recommend using the IBM Skills Network Labs environment, which comes pre-installed with the necessary tools, including Docker, to simplify your setup process. This platform is optimized for use with the latest versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.
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