From predicting weather and modeling fluids to optimizing financial markets, traditional simulations rely on solving partial differential equations (PDEs) or using data-driven machine-learning models. However, differential equations solvers are often computationally expensive, and pure data-driven approaches struggle with limited or noisy data. Physics-Informed Machine Learning (PI-ML) offers a powerful alternative by embedding known physics of the problem directly into deep learning models, combining the strengths of both worlds.

This talk will introduce Physics-Informed Neural Networks (PINNs) and extend beyond them to more advanced approaches like Neural Operators. We’ll explore how these techniques are transforming real-world applications, from fluid simulations in engineering to climate forecasting and even economic modeling.

Attendees will learn:

• How the "known physics of the problem" can enhance ML models for better generalization and efficiency.
• Glimpse of implementation using Python frameworks like PyTorch, Deep-XDE, and NVIDIA PhysicsNeMo (Modulus)
• Case studies where PI-ML models outperform traditional methods.

No deep math or PDE knowledge is required. This session is designed to be insightful, engaging, and accessible to ML practitioners, engineers, and researchers curious about Scientific Machine Learning.