Problem
Research questions and friction points this paper is trying to address.
symbolic regression
stoichiometric ODE systems
physical laws
FluxDisco: Symbolic Regression for Stoichiometric Dynamical Systems via Monte Carlo Graph Search
🤖 AI Summary
本文提出FluxDisco框架，通过蒙特卡洛图搜索算法，结合已知化学计量学信息，从噪声数据中准确恢复符合物理定律的动力学方程。
📝 Abstract
Dynamical symbolic regression methods identify governing differential equations from noisy data, balancing interpretability and predictive accuracy. However, standard methods often produce expressions that violate known physical laws. To address this, we propose FluxDisco, a physics-informed framework tailored for flux-based, stoichiometric ODE systems. By leveraging a known stoichiometry, we reduce the expression search space and ensure physical adherence. Our framework adapts the Monte Carlo Graph Search algorithm for the unique challenges associated with joint flux discovery of stoichiometric systems. We evaluate our method across a range of physical and biological systems, demonstrating its ability to accurately recover governing dynamics through interpretable equations.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Symbolic Regression
Stoichiometric ODE Systems
Monte Carlo Graph Search
Physics-informed Framework
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