Physics-Informed Neural Networks (PINNs)
Embed differential equations (ODEs/PDEs) directly into neural network loss functions using PyTorch and Automatic Differentiation.
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.
W1
Mathematical Foundations of PINNs
Mathematical Foundations of PINNs
Master the core concepts of mathematical foundations of pinns.
4 videos•118m
3 readings
4 topics
1 homework
References
Week 1: Mathematical Foundations of PINNs
Week 2: Solving Ordinary Differential Equations (ODEs) with PyTorch
Week 3: Solving Partial Differential Equations (PDEs)
Week 4: Inverse Problems & Parameter Estimation
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