35 - Beyond the Patterns - Nils Thuerey – Differentiable Physics Simulations for Deep Learning/ClipID:35513 previous clip next clip

Recording date 2021-07-07

Via

Free

Language

English

Organisational Unit

Friedrich-Alexander-Universität Erlangen-Nürnberg

Producer

Friedrich-Alexander-Universität Erlangen-Nürnberg

It’s a great pleasure to welcome FAU Alumnus Nils Thuerey to our lab for an invited presentation!

Abstract

In this talk, I will focus on the possibilities that arise from recent advances in the area of deep learning for physical simulations. In this context, especially the Navier-Stokes equations represent an interesting and challenging advection-diffusion PDE that poses a variety of challenges for deep learning methods.

In particular, I will focus on differentiable physics solvers within the larger field of differentiable programming. Differentiable solvers are very powerful tools to guide deep learning processes and support finding desirable solutions. The existing numerical methods for efficient solvers can be leveraged within learning tasks to provide crucial information in the form of reliable gradients to update the weights of a neural network. Interestingly, it turns out to be beneficial to combine supervised and physics-based approaches. The former poses a much simpler learning task by providing explicit reference data that is typically pre-computed. Physics-based learning on the other hand can provide gradients for a larger space of states that are only encountered during training runs. Here, differentiable solvers are particularly powerful to, e.g., provide neural networks with feedback about how inferred solutions influence the long-term behavior of a physical model.

I will demonstrate this concept with several examples from learning to reduce numerical errors, over long-term planning and control, to generalization. I will conclude by discussing current limitations and by giving an outlook about promising future directions.

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 flow problems. Beyond latent-space simulation algorithms and generative models, he’s currently especially interested in learning algorithms powered by differentiable solvers.

References
https://github.com/tum-pbs/PhiFlow
https://ge.in.tum.de/publications/

This video is released under CC BY 4.0. Please feel free to share and reuse.

For reminders to watch the new video follow on Twitter or LinkedIn. Also, join our network for information about talks, videos, and job offers in our Facebook and LinkedIn Groups.

Music Reference: 
Damiano Baldoni - Thinking of You (Intro)
Damiano Baldoni - Poenia (Outro)

More clips in this category "Friedrich-Alexander-Universität Erlangen-Nürnberg"

2021-10-21
Studon
protected