[1H3-GS-1b] 基礎・理論：モデル化

[1H3-GS-1b-05] Deep Learning-Based Discrete-Time Simulation of Physical Phenomena and its Energetic Behavior

〇Takashi Matsubara¹, Takehiro Aoshima¹, Ai Ishikawa², Takaharu Yaguchi² (1. Osaka University, 2. Kobe University)

Keywords:deep learning, physics simulation, numerical integration, energy conservation law

Machine learning-based modeling of physics phenomena is expected to accelerate simulations and to find a new phenomenon. Physics phenomena are often associated with conservation and dissipation laws of certain quantities. A dependable simulation must guarantee the laws of physics in discrete time. In this paper, we propose a deep learning-based modeling that ensures such laws of physics, and automatic discrete differentiation algorithm, which is an algorithm that ensures the laws in discrete-time. Experimental results demonstrate that the proposed framework ensures the energy conservation and dissipation laws up to the rounding error, and it learns a given dynamics more accurately than existing methods based on ordinary numerical integrators.