The Symbiosis of Deep Learning and Differential Equations

Luca Celotti · Kelly Buchanan · Jorge Ortiz · Patrick Kidger · Stefano Massaroli · Michael Poli · Lily Hu · Ermal Rrapaj · Martin Magill · Thorsteinn Jonsson · Animesh Garg · Murtadha Aldeer

Abstract Workshop Website [ GatherTown
Tue 14 Dec, 3:45 a.m. PST


Deep learning can solve differential equations, and differential equations can model deep learning. What have we learned and where to next?

The focus of this workshop is on the interplay between deep learning (DL) and differential equations (DEs). In recent years, there has been a rapid increase of machine learning applications in computational sciences, with some of the most impressive results at the interface of DL and DEs. These successes have widespread implications, as DEs are among the most well-understood tools for the mathematical analysis of scientific knowledge, and they are fundamental building blocks for mathematical models in engineering, finance, and the natural sciences. This relationship is mutually beneficial. DL techniques have been used in a variety of ways to dramatically enhance the effectiveness of DE solvers and computer simulations. Conversely, DEs have also been used as mathematical models of the neural architectures and training algorithms arising in DL.

This workshop will aim to bring together researchers from each discipline to encourage intellectual exchanges and cultivate relationships between the two communities. The scope of the workshop will include important topics at the intersection of DL and DEs.

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