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
Master the core concepts of mathematical foundations of pinns.
4 videos•118m
3 readings
4 topics
1 homework
W2
Solving Ordinary Differential Equations (ODEs) with PyTorch
Gain hands-on experience with solving ordinary differential equations (odes) with pytorch.
4 videos•58m
3 readings
4 topics
1 homework
W3
Solving Partial Differential Equations (PDEs)
Understand the architecture behind solving partial differential equations (pdes).
4 videos•65m
3 readings
4 topics
1 homework
W4
Inverse Problems & Parameter Estimation
Implement production-grade inverse problems & parameter estimation.
4 videos•123m
3 readings
4 topics
1 homework
01
Learn
Watch curated videos and read study resources
02
Practice
Practice what you learned
03
Build Projects
Build projects using your new gained knowledge
04
Submit & Verify
Submit your project and get verified by our system
References
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