Interpretable Neural System Dynamics: Combining Deep Learning with System Dynamics Modeling to Support Critical Applications

TL;DR

This paper proposes an interpretable neural system dynamics framework that combines deep learning's predictive power with the transparency of system dynamics modeling, aiming to enhance causal understanding and scalability in critical applications.

Contribution

It introduces a novel Neural System Dynamics pipeline integrating interpretability and causal machine learning to bridge DL and SD limitations.

Findings

Framework demonstrates improved interpretability and causal insights.

Validated on autonomous transportation system applications.

Enhances scalability and safety in complex systems.

Abstract

The objective of this proposal is to bridge the gap between Deep Learning (DL) and System Dynamics (SD) by developing an interpretable neural system dynamics framework. While DL excels at learning complex models and making accurate predictions, it lacks interpretability and causal reliability. Traditional SD approaches, on the other hand, provide transparency and causal insights but are limited in scalability and require extensive domain knowledge. To overcome these limitations, this project introduces a Neural System Dynamics pipeline, integrating Concept-Based Interpretability, Mechanistic Interpretability, and Causal Machine Learning. This framework combines the predictive power of DL with the interpretability of traditional SD models, resulting in both causal reliability and scalability. The efficacy of the proposed pipeline will be validated through real-world applications of the EU-funded AutoMoTIF project, which is focused on autonomous multimodal transportation systems. The long-term goal is to collect actionable insights that support the integration of explainability and safety in autonomous systems.