Physics-Informed Neural Networks (PINNs)

Embed differential equations (ODEs/PDEs) directly into neural network loss functions using PyTorch and Automatic Differentiation.

Created Byeulerfoldeulerfold
4 weeks
Jul 23
to start learning
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About this Course

Embed differential equations (ODEs/PDEs) directly into neural network loss functions using PyTorch and Automatic Differentiation. This AI/ML & Scientific Computing curriculum is designed to give you hands-on experience and deep conceptual understanding. Across 4 intensive modules, you'll tackle real-world challenges and build practical projects that reinforce your learning. By the end of this journey, you'll have the skills and proof of work to demonstrate your expertise.

What you'll learn

Master the core concepts of mathematical foundations of pinns.
Gain hands-on experience with solving ordinary differential equations (odes) with pytorch.
Understand the architecture behind solving partial differential equations (pdes).
Implement production-grade inverse problems & parameter estimation.

Prerequisites

intermediate Level

Requires basic familiarity with the tech stack.

  • TCP/UDP fundamentals
  • Socket programming basics

Ideal for

Scientific Computing Researchers

AI/ML & Scientific Computing Professionals
Tech Enthusiasts
W1

Mathematical Foundations of PINNs

Master the core concepts of mathematical foundations of pinns.

4 videos118m
3 readings
4 topics
1 homework
Learn

Topics

1.1
Forward vs Inverse Problems
Learning Physics Informed Machine Learning Part 2- Inverse Physics Informed Neural Networks (PINNs)
31 minutes
1.2
Embedding Physical Laws in Machine Learning
All Machine Learning algorithms explained in 17 min
16 minutes
1.3
Automatic Differentiation (Autograd)
What is Automatic Differentiation?
14 minutes
1.4
Loss Function Formulations
Physics Informed Neural Networks explained for beginners | From scratch implementation and code
57 minutes
W2

Solving Ordinary Differential Equations (ODEs) with PyTorch

Gain hands-on experience with solving ordinary differential equations (odes) with pytorch.

4 videos58m
3 readings
4 topics
1 homework
Learn
W3

Solving Partial Differential Equations (PDEs)

Understand the architecture behind solving partial differential equations (pdes).

4 videos65m
3 readings
4 topics
1 homework
Learn
W4

Inverse Problems & Parameter Estimation

Implement production-grade inverse problems & parameter estimation.

4 videos123m
3 readings
4 topics
1 homework
Learn
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