Nils Thürey

Lecture title:

"Unifying Forces: Towards the Convergence of Numerical Simulations and Machine Learning"

Abstract:

In this presentation, I will delve into recent advancements from the area of deep learning for physics simulations. A key focus is on the utilization of numerical solvers capable of providing gradient information, i.e. "differentiable simulators". These solvers seamlessly integrate with deep learning algorithms, presenting several advantages in practical scenarios, particularly in the context of flow simulations. However, the availability of gradient computation is not ubiquitous in many existing fluid simulation environments. Consequently, I will demonstrate a strategic approach to leverage non-differentiable simulators, serving as a transitional step and a middle ground in this context. As an outlook, I will explore the potential integration of these methods with diffusion modeling techniques, which offer powerful tools for handling uncertainties.

Short Bio:

Nils Thuerey is an Associate-Professor at the Technical University of Munich (TUM). He focuses on deep-learning methods for physical systems, with an emphasis on fluid flows. Together with his research group he targets new methods for the tight and seamless integration of learning algorithms with classical numerical methods. This encompasses differentiable numerical solvers and architectural inductive biases. He's additionally interested in neural surrogates, learned reduced-order simulations, diffusion models, and visual reconstructions.

Nils Thürey, TU Munich, Germany