Introduction
This bi-monthly seminar series explores real-world applications of physics-informed machine learning (Φ-ML) methods to the engineering practice. They cover a wide range of topics, offering a cross-sectional view of the state of the art on Φ-ML research, worldwide.
Participants have the opportunity to hear from leading researchers and learn about the latest developments in this emerging field. These seminars also offer the chance to identify and spark collaboration opportunities.
About the event
Machine learning (ML) is increasingly playing a pivotal role in spatiotemporal modeling. A number of open questions remain on the best learning strategies to maximize the utility of machine learning while ensuring the validity of such predictions, particularly in limited data scenarios. This talk will focus on exploring machine learning strategies for neural PDE solvers, with an emphasis on broad learning strategies that are applicable across a wide variety of systems and neural network architectures. Some topics I will discuss include: using self-supervised learning to change the basis of learning with spectral methods to solve fluid dynamics and transport PDE problems, and “simulation-in-the-loop” approaches via incorporating PDE-constrained optimization as a layer in neural networks. In each of these settings, I will discuss how ML methods can be used with numerical methods through fully differentiable settings.
Watch on demand:
Recording will be uploaded after the seminar.
