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The paper introduces Weak-Pareto, a method that uses adjoint-consistent weak formulations and Pareto-based subset selection to discover fractional differential equations from noisy data, recovering parsimonious structures robustly across benchmarks.
This paper introduces dynamics-aware preprocessing via Koopman-based upsampling (DMD/EDMD) to improve derivative estimation and equation discovery from sparse, noisy data, benchmarking on ODE and PDE systems.
FLARE is a forced latent autoencoder that discovers compact response coordinates and sparse input-dependent latent dynamics from high-dimensional observations of forced physical systems, enabling long-horizon forecasting under unseen inputs.