FluxDisco：基于蒙特卡洛图搜索的化学计量动态系统符号回归方法
FluxDisco: Symbolic Regression for Stoichiometric Dynamical Systems via Monte Carlo Graph Search
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中文总结 AI 辅助
该研究针对动态符号回归方法易生成违背物理定律表达式的问题，提出FluxDisco框架，适配蒙特卡洛图搜索算法，在多类物理与生物系统上可通过可解释方程准确恢复控制动力学。
中文摘要 AI 辅助
动态符号回归方法可从含噪数据中识别控制微分方程，兼顾可解释性与预测准确性，但常规方法常生成违背已知物理定律的表达式。为解决该问题，我们提出FluxDisco，一种专为基于通量的化学计量常微分方程（ODE）系统设计的物理信息框架。利用已知化学计量关系，我们缩小表达式搜索空间并确保符合物理规律，该框架适配蒙特卡洛图搜索（Monte Carlo Graph Search）算法，以应对化学计量系统联合通量发现的独特挑战。我们在一系列物理与生物系统上评估该方法，证明其能通过可解释方程准确恢复控制动力学。
英文摘要
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.
发表机构
- Lancaster University（兰卡斯特大学）
- University of Oslo（奥斯陆大学）
机构由 AI 辅助整理，请以论文原文为准。
