AI Insight
Researchers introduce Edwin, a computational framework that combines dimensionality reduction with symbolic model discovery to extract interpretable, low-dimensional governing equations from high-dimensional data. Edwin uses the dynamic maximum entropy (DME) principle to simultaneously compress complex observations, identify sparse mathematical models of the underlying dynamics, and link learned features to physically meaningful variables. The framework was validated across multiple simulated systems (including stochastic processes, self-assembling particles, and neural networks) and on experimental RNA-liposome aggregation data, successfully recovering generalizable, physically interpretable models in all cases.
Why it matters
Edwin addresses a longstanding trade-off in data-driven modeling between interpretability and accuracy, offering a unified tool that could accelerate scientific discovery in fields such as biophysics, neuroscience, and soft matter physics by automating the extraction of meaningful governing equations from complex experimental datasets.
arXiv:2605.16724v1 Announce Type: new
Abstract: Models (i.e., governing equations) are fundamental to science and engineering. Advances in data acquisition now make it possible to extract interpretable, low dimensional descriptions from high dimensional observations. However, existing approaches sacrifice either interpretability for reconstruction accuracy or infer symbolic dynamics without relating latent coordinates to physically meaningful observables. Here we present Edwin (maximum entropy driven compression with interpretable nonlinear model discovery), a unified framework that simultaneously performs dimensionality reduction using the dynamic maximum entropy (DME) principle and discovers sparse symbolic models governing latent dynamics, as well as the coupling between learned features and external metadata. We validate Edwin on diverse simulated systems, including stochastic diffusion, the Ornstein-Uhlenbeck process, self assembling particles, spiking neural populations, and low rank recurrent neural networks, as well as on a noisy experimental time series of aggregating RNA-liposome complexes. Across all systems, Edwin recovers low dimensional symbolic models that are physically interpretable and generalize to unseen conditions. Together, these results establish Edwin as a powerful framework for inferring interpretable, low dimensional dynamics directly from high dimensional data.
Source: Discovering interpretable low-dimensional dynamics using maximum entropy