Derivative-Free Domain-Informed Data-Driven Discovery of Sparse Kinetic Models.

ABSTRACT: Developing data-driven kinetic models from reaction data is valuable for inferring the underlying reactions and designing reactive processes without needing first-principles models. However, recently developed techniques to learn interpretable dynamical models from data are susceptible to inherent experimental noise, especially in reaction kinetics data. Here, we address these issues by (1) employing a new derivative-free technique for sparse identification of dynamical equations that approximates the integral rather than the derivative (which we call as DF-SINDy) and (2) including domain information such as mass balance and chemistry information. We demonstrate this using retrospective examples to recover the true (known) governing equations from synthetic data under varying noise

SUBMITTER: Prabhu S 

PROVIDER: S-EPMC11803628 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature