New Algorithm Recovers Hidden Equations from Noisy High-Dimensional Data Using Multi-View Learning
Researchers have developed DYSCO, a machine learning algorithm that can extract latent dynamical systems and their governing equations from noisy, high-dimensional observations by leveraging multiple independent views of the same process. The method combines representation learning with system identification and includes theoretical guarantees for accurate recovery under realistic conditions including nonlinear observations. This advance could improve scientific discovery across fields where underlying dynamics must be inferred from complex measurements, such as neuroscience and physics.
DYSCO is a multi-view temporal contrastive learning algorithm designed to solve a fundamental problem in scientific computing: identifying the hidden equations that govern dynamical systems from noisy, high-dimensional data. The approach works by using multiple independent noisy observations of the same underlying process to separate true signal from noise, then parameterizes the dynamics using a structured functional basis to enable symbolic recovery of governing equations. The researchers provide theoretical guarantees for strong identification up to an affine indeterminacy, extending previous identifiability results to realistic settings with nonlinear observations. Empirical validation demonstrates accurate recovery of both latent trajectories and flow fields across diverse dynamical regimes—including chaotic, oscillatory, and metastable systems—under both Gaussian and Poisson observation noise, with the latter being particularly relevant for neural recordings.
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- arXiv q-bioCenter
Extracting Governing Equations from Latent Dynamics via Multi-View Contrastive Learning
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