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Explore Physics-Informed Machine Learning (PI-ML) in this 24-minute conference talk that demonstrates how to embed scientific laws directly into deep learning models to create more efficient and accurate simulations. Learn about Physics-Informed Neural Networks (PINNs) as a powerful alternative to traditional computational methods that combines the strengths of physics-based differential equation solvers with data-driven machine learning approaches. Discover how known physics can enhance ML models for better generalization and efficiency, particularly when dealing with limited or noisy data. Examine practical implementations using open-source Python libraries including PyTorch, Deep-XDE, and NVIDIA PhysicsNeMo (Modulus) through hands-on examples and code demonstrations. Analyze real-world case studies where PI-ML models outperform traditional methods in applications ranging from fluid simulations in engineering to weather prediction and climate forecasting, as well as financial market optimization. Understand how this approach addresses the computational expense of differential equation solvers while overcoming the limitations of pure data-driven models. Gain insights into the transformative potential of Scientific Machine Learning across various domains without requiring deep mathematical background, making this content accessible to ML practitioners, engineers, and researchers interested in the intersection of physics and machine learning.
