Pages that link to "Item:Q5161113"

The following pages link to SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics (Q5161113):

Displaying 31 items.

• SINDy-PI (Q56067) (← links)
• Deep learning of conjugate mappings (Q2077602) (← links)
• Design of the monodomain model by artificial neural networks (Q2098864) (← links)
• Learning non-Markovian physics from data (Q2128336) (← links)
• WeakIdent: weak formulation for identifying differential equation using narrow-fit and trimming (Q2699369) (← links)
• Equation discovery from data: promise and pitfalls, from rabbits to Mars (Q5039484) (← links)
• Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue (Q6044215) (← links)
• Model selection via reweighted partial sparse recovery (Q6056227) (← links)
• Learning sparse nonlinear dynamics via mixed-integer optimization (Q6060066) (← links)
• Auxiliary functions as Koopman observables: data-driven analysis of dynamical systems via polynomial optimization (Q6066022) (← links)
• A framework for machine learning of model error in dynamical systems (Q6076655) (← links)
• Regression-Based Projection for Learning Mori–Zwanzig Operators (Q6084965) (← links)
• Derivative-based SINDy (DSINDy): addressing the challenge of discovering governing equations from noisy data (Q6099213) (← links)
• Deep learning discrete calculus (DLDC): a family of discrete numerical methods by universal approximation for STEM education to frontier research (Q6109277) (← links)
• \textit{FastSVD-ML-ROM}: a reduced-order modeling framework based on machine learning for real-time applications (Q6116133) (← links)
• Sparse identification of dynamical systems by reweighted \(l_1\)-regularized least absolute deviation regression (Q6121816) (← links)
• Structured model selection via ℓ1−ℓ2 optimization (Q6141559) (← links)
• Learning the flux and diffusion function for degenerate convection-diffusion equations using different types of observations (Q6492247) (← links)
• Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics (Q6550128) (← links)
• Machine learning enhanced Hankel dynamic-mode decomposition (Q6550749) (← links)
• Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale dynamical systems (Q6554903) (← links)
• Data-driven discovery of interpretable Lagrangian of stochastically excited dynamical systems (Q6557792) (← links)
• Sparse identification of nonlinear dynamical systems via non-convex penalty least squares (Q6561186) (← links)
• Data-driven identification of dynamical models using adaptive parameter sets (Q6561192) (← links)
• Physics-informed genetic programming for discovery of partial differential equations from scarce and noisy data (Q6589933) (← links)
• On higher order drift and diffusion estimates for stochastic SINDy (Q6592244) (← links)
• Dynamical system identification, model selection, and model uncertainty quantification by Bayesian inference (Q6604850) (← links)
• Model reduction of dynamical systems with a novel data-driven approach: the RC-HAVOK algorithm (Q6604859) (← links)
• Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER) (Q6621776) (← links)
• How much can one learn a partial differential equation from its solution? (Q6645956) (← links)
• Data-driven variational method for discrepancy modeling: dynamics with small-strain nonlinear elasticity and viscoelasticity (Q6648552) (← links)