Short course on physics informed deep learning

Abstract

In recent years, physics-informed deep learning (PIDL) has emerged as a powerful tool to solve many problems in the field of computational science. The main idea of PIDL is to incorporate the governing physical laws into a deep learning framework. The PIDL can smoothly integrate the sparse, noisy, and multi-fidelity data along with the governing equations and thereby recast the original PDE problem into an equivalent optimization problem. This approach has various advantages, including the ability to handle ill-posed inverse problems easily. Furthermore, it is a mesh-free approach and is capable of overcoming the curse of dimensionality. In this mini-workshop, I will cover the fundamentals of deep learning as well as physics-informed deep learning through hands-on coding exercises. I will also discuss some advanced PIDL topics, such as its current capabilities, limitations, and various applications, as this is still an active area of research.

• Introduction to Deep Learning and Physics-Informed Deep Learning (10:00 AM - 12:00 PM)
• Performance Improvement Techniques for Physics-Informed Deep Learning (2:00 PM – 4:00 PM)

Each lecture includes hands-on coding exercises. The recommended software:

1. TensorFlow 1 or 2 (ML library)
2. Python 3.6
3. Latex (for plotting figures)

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