Identifiability Challenges in Sparse Linear Ordinary Differential Equations

TL;DR

This paper investigates the identifiability of sparse linear ODE systems, revealing that unlike dense systems, sparsity often leads to unidentifiability with significant practical implications.

Contribution

It characterizes the conditions under which sparse linear ODEs are unidentifiable and provides probabilistic bounds, addressing a gap in existing research.

Findings

Sparse systems are unidentifiable with positive probability in relevant regimes.

Empirical analysis shows practical unidentifiability in state-of-the-art methods.

Theoretical limitations persist despite inductive biases or optimization techniques.

Abstract

Dynamical systems modeling is a core pillar of scientific inquiry across natural and life sciences. Increasingly, dynamical system models are learned from data, rendering identifiability a paramount concept. For systems that are not identifiable from data, no guarantees can be given about their behavior under new conditions and inputs, or about possible control mechanisms to steer the system. It is known in the community that "linear ordinary differential equations (ODE) are almost surely identifiable from a single trajectory." However, this only holds for dense matrices. The sparse regime remains underexplored, despite its practical relevance with sparsity arising naturally in many biological, social, and physical systems. In this work, we address this gap by characterizing the identifiability of sparse linear ODEs. Contrary to the dense case, we show that sparse systems are unidentifiable with a positive probability in practically relevant sparsity regimes and provide lower bounds for this probability. We further study empirically how this theoretical unidentifiability manifests in state-of-the-art methods to estimate linear ODEs from data. Our results corroborate that sparse systems are also practically unidentifiable. Theoretical limitations are not resolved through inductive biases or optimization dynamics. Our findings call for rethinking what can be expected from data-driven dynamical system modeling and allows for quantitative assessments of how much to trust a learned linear ODE.