Solving an inverse problem consists in finding the unknown parameters of a system from observations. This task is particularly challenging when observations have a strong stochastic component, producing diverse yet equivalent patterns. Self-organizing systems are a notable case: simple local rules can generate complex stochastic patterns. Many natural systems rely on such processes, making self-organization central to understanding natural phenomena. Inverse methods based on direct comparison of observed values struggle in this setting, as such comparisons cannot capture feature similarities between variable outcomes; as a result, the loss fails to meaningfully inform the optimizer. In this work, we show that the difficulty posed by stochasticity in observable patterns can be overcome by reformulating the inverse problem in an embedding space. By mapping stochastic patterns onto an invariant embedding space, where equivalent patterns have similar representations, we can effectively recover unknown causal parameters without the need for handcrafted objective functions or heuristics. We show that embedding models trained on large image datasets produce robust representations that capture these invariances. 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.

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