AI for Partial Differential Equations
Overview
Partial Differential Equations (PDEs) lie at the heart of many problems in science and engineering. Because of the ever-growing complexity of scientific problems and volumes of data, scientists and engineers are increasingly looking towards Artificial Intelligence (AI), with its rapid predictive power and ability to learn from data previously unknown relationships, to tackle challenging forward and inverse problems involving PDEs. At the forefront of this movement is the fusion of AI with physical, mathematical, and domain knowledge, often called Scientific Machine Learning or Physics-Informed Machine Learning.
This webinar will delve into the transformative potential of physics-based, AI-driven techniques for PDEs, like Physics-Informed Neural Networks (PINNs), which integrate physical laws directly into neural network training, Fourier Neural Operator (FNO), which leverages Fourier transforms for resolution-invariant operator learning, and finally Physics-Informed Neural Operator (PINO), which combines the strength of PINNs and FNO. Practical demonstrations on implementing these methodologies in MATLAB will provide attendees with hands-on insights and tools for their research and projects. Join us as we explore this cutting-edge intersection of AI and PDEs, uncovering new possibilities and applications in science and engineering.
Highlights
- Physics Informed Neural Networks (PINNs) with Deep Learning Toolbox, and optionally Symbolic Math Toolbox and PDE Toolbox
- Fourier Neural Operator with Deep Learning Toolbox
- Physics-Informed Neural Operator with Deep Learning Toolbox
About the Presenter
Mae Markowski is the Product Manager for the Partial Differential Equation Toolbox and the Symbolic Math Toolbox, and a marketing lead for AIxPhysics at MathWorks. Mae earned her Ph.D. in Computational and Applied Mathematics from Rice University, where her research centered around PDE-constrained optimization under uncertainty. She enjoys using her background in applied math to advance and promote symbolic computing, PDE and ODE solvers, and physics-informed AI capabilities at MathWorks.
Product Focus
This event is part of a series of related topics. View the full list of events in this series.
