Tag
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.
Introduces Weak-form Kernel Ridge Regression (WKRR) for learning dynamical systems from noisy measurements, combining a weak formulation with kernel ridge regression to filter noise and improve accuracy. The method outperforms baseline methods on chaotic benchmarks up to 64 dimensions and 15,000-dimensional real-world fluid data.