Solving Inverse Problems in Stochastic Self-Organizing Systems through Invariant Representations

TL;DR

This paper introduces a novel inverse modeling approach that uses invariant visual embeddings to accurately recover causal parameters from stochastic self-organizing systems, overcoming limitations of traditional pixel-based metrics.

Contribution

The work presents a new method leveraging invariant visual embeddings to solve inverse problems in stochastic self-organizing systems without handcrafted objectives.

Findings

Successfully recovers parameters in physical, biological, and social models.

Effective on real biological pattern data.

Handles high stochasticity in pattern outcomes.

Abstract

Self-organizing systems demonstrate how simple local rules can generate complex stochastic patterns. Many natural systems rely on such dynamics, making self-organization central to understanding natural complexity. A fundamental challenge in modeling such systems is solving the inverse problem: finding the unknown causal parameters from macroscopic observations. This task becomes particularly difficult when observations have a strong stochastic component, yielding diverse yet equivalent patterns. Traditional inverse methods fail in this setting, as pixel-wise metrics cannot capture feature similarities between variable outcomes. In this work, we introduce a novel inverse modeling method specifically designed to handle stochasticity in the observable space, leveraging the capacity of visual embeddings to produce robust representations that capture perceptual invariances. By mapping the pattern representations onto an invariant embedding space, we can effectively recover unknown causal parameters without the need for handcrafted objective functions or heuristics. We evaluate the method on three self-organizing systems: a physical, a biological, and a social one; namely, a reaction-diffusion system, a model of embryonic development, and an agent-based model of social segregation. We show that the method reliably recovers parameters despite stochasticity in the pattern outcomes. We further apply the method to real biological patterns, highlighting its potential as a tool for both theorists and experimentalists to investigate the dynamics underlying complex stochastic pattern formation.