This is the public, feature-limited version of the conference webpage. After Registration and login please visit the full version.

Deep Energy-based Modeling of Discrete-Time Physics

Takashi Matsubara, Ai Ishikawa, Takaharu Yaguchi

Oral presentation: Orals & Spotlights Track 06: Dynamical Sys/Density/Sparsity
on 2020-12-08T06:00:00-08:00 - 2020-12-08T06:15:00-08:00
Poster Session 2 (more posters)
on 2020-12-08T09:00:00-08:00 - 2020-12-08T11:00:00-08:00
Abstract: Physical phenomena in the real world are often described by energy-based modeling theories, such as Hamiltonian mechanics or the Landau theory, which yield various physical laws. Recent developments in neural networks have enabled the mimicking of the energy conservation law by learning the underlying continuous-time differential equations. However, this may not be possible in discrete time, which is often the case in practical learning and computation. Moreover, other physical laws have been overlooked in the previous neural network models. In this study, we propose a deep energy-based physical model that admits a specific differential geometric structure. From this structure, the conservation or dissipation law of energy and the mass conservation law follow naturally. To ensure the energetic behavior in discrete time, we also propose an automatic discrete differentiation algorithm that enables neural networks to employ the discrete gradient method.

Preview Video and Chat

To see video, interact with the author and ask questions please use registration and login.