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We introduce a method for inferring an explicit PDE from a data samplegenerated by previously unseen dynamics, based on a learned context. Thetraining phase integrates knowledge of the form of the equation with adifferential scheme, while the inference phase yields a PDE that fits the datasample and enables both signal prediction and data explanation. We includeresults of extensive experimentation, comparing our method to SOTA approaches,together with ablation studies that examine different flavors of our solutionin terms of prediction error and explainability.

CONFIDE: Contextual Finite Differences Modelling of PDEs