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Our first lecture will be delivered by <a href="https://www.linkedin.com/feed/?nis=true#" rel="noopener noreferrer" target="_blank">Satwik Sinha</a> on PINNS. <a href="https://www.linkedin.com/feed/?nis=true#" rel="noopener noreferrer" target="_blank">Satwik</a>, is a distinguished AI researcher and software engineer with more than a decade of hands-on experience, is currently pursuing a master's degree in computer science at Illinois Tech in Chicago. He will deliver a lecture about Physics-Informed Neural Networks (PINNs). Physics-Informed Neural Networks (PINNs) are transforming how we approach complex scientific and engineering challenges. Regular and partial differential equations (ODEs and PDEs) can be used to solve real world problems problems. PINNs are a powerful way to do this because they seamlessly incorporate physical laws into neural network training. Why Attend? 1) Learn how PINNs represent solutions to PDEs directly within neural networks.2) Discover how crafting specific loss functions can enforce physical constraints, ensuring solutions remain true to fundamental principles.3) Explore the use of automatic differentiation and gradient descent in training these sophisticated models.4) See real-world applications of PINNs across diverse fields like fluid dynamics, material science, and beyond.

The AI Hour Season 1 Episode 1: PINNs

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