Van der Pol oscillator

The Van der Pol oscillator is a benchmark dataset used to evaluate the performance of probabilistic extensions of physics-informed neural networks (PINNs) in uncertainty quantification for inverse problems governed by partial differential equations.

Papers using Van der Pol oscillator (9)

Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics2025 · 3 citesUncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles2026Trajectory-Optimized Time Reparameterization for Learning-Compatible Reduced-Order Modeling of Stiff Dynamical Systems2026Efficient Training of Physics-enhanced Neural ODEs via Direct Collocation and Nonlinear Programming2025Physics-Informed Echo State Networks for Modeling Controllable Dynamical Systems2024Machine learning in parameter estimation of nonlinear systems2023 · 2 citesModelling of physical systems with a Hopf bifurcation using mechanistic models and machine learning2022 · 1 citesSolving Differential Equations using Physics-Informed Deep Equilibrium Models2024 · 1 citesKAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems2024