SpReME: Sparse Regression for Multi-Environment Dynamic Systems

• URL: http://arxiv.org/abs/2302.05942v1
• Date: Sun, 12 Feb 2023 15:45:50 GMT
• Title: SpReME: Sparse Regression for Multi-Environment Dynamic Systems
• Authors: MoonJeong Park, Youngbin Choi, Namhoon Lee and Dongwoo Kim
• Abstract summary: We develop a method of sparse regression dubbed SpReME to discover the major dynamics that underlie multiple environments. We demonstrate that the proposed model captures the correct dynamics from multiple environments over four different dynamic systems with improved prediction performance.
• Score: 6.7053978622785415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning dynamical systems is a promising avenue for scientific discoveries. However, capturing the governing dynamics in multiple environments still remains a challenge: model-based approaches rely on the fidelity of assumptions made for a single environment, whereas data-driven approaches based on neural networks are often fragile on extrapolating into the future. In this work, we develop a method of sparse regression dubbed SpReME to discover the major dynamics that underlie multiple environments. Specifically, SpReME shares a sparse structure of ordinary differential equation (ODE) across different environments in common while allowing each environment to keep the coefficients of ODE terms independently. We demonstrate that the proposed model captures the correct dynamics from multiple environments over four different dynamic systems with improved prediction performance.

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