SpReME: Sparse Regression for Multi-Environment Dynamic Systems
This work addresses the problem of robustly modeling multi-environment dynamics for scientific discovery, representing an incremental improvement over existing methods.
The authors tackled the challenge of learning dynamical systems across multiple environments by developing SpReME, a sparse regression method that shares ODE structure while allowing environment-specific coefficients, resulting in improved prediction performance on four dynamic systems.
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.