A Self-Adaptive Penalty Method for Integrating Prior Knowledge Constraints into Neural ODEs

TL;DR

This paper introduces a self-adaptive penalty method for Neural ODEs that incorporates prior knowledge constraints, improving model interpretability, accuracy, and robustness in modeling natural systems.

Contribution

It presents a novel self-adaptive penalty algorithm that dynamically adjusts penalties, enhancing Neural ODEs' ability to model constrained natural systems with increased interpretability.

Findings

Effective modeling of constrained systems like population growth and chemical reactions.

Outperforms other penalty Neural ODE approaches and vanilla Neural ODE.

Produces more accurate, robust, and interpretable models.

Abstract

The continuous dynamics of natural systems has been effectively modelled using Neural Ordinary Differential Equations (Neural ODEs). However, for accurate and meaningful predictions, it is crucial that the models follow the underlying rules or laws that govern these systems. In this work, we propose a self-adaptive penalty algorithm for Neural ODEs to enable modelling of constrained natural systems. The proposed self-adaptive penalty function can dynamically adjust the penalty parameters. The explicit introduction of prior knowledge helps to increase the interpretability of Neural ODE -based models. We validate the proposed approach by modelling three natural systems with prior knowledge constraints: population growth, chemical reaction evolution, and damped harmonic oscillator motion. The numerical experiments and a comparison with other penalty Neural ODE approaches and \emph{vanilla} Neural ODE, demonstrate the effectiveness of the proposed self-adaptive penalty algorithm for Neural ODEs in modelling constrained natural systems. Moreover, the self-adaptive penalty approach provides more accurate and robust models with reliable and meaningful predictions.