Pages that link to "Item:Q137310"
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The following pages link to Discovering governing equations from data by sparse identification of nonlinear dynamical systems (Q137310):
Displaying 50 items.
- Synchronization of reservoir computing models via a nonlinear controller (Q2096779) (← links)
- Full and reduced order model consistency of the nonlinearity discretization in incompressible flows (Q2096852) (← links)
- Design of the monodomain model by artificial neural networks (Q2098864) (← links)
- A Bayesian approach for data-driven dynamic equation discovery (Q2102994) (← links)
- Finite-data error bounds for Koopman-based prediction and control (Q2105227) (← links)
- One-shot learning of stochastic differential equations with data adapted kernels (Q2111726) (← links)
- Data-driven reduced-order modeling for nonautonomous dynamical systems in multiscale media (Q2112501) (← links)
- Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems (Q2112549) (← links)
- Robust modeling of unknown dynamical systems via ensemble averaged learning (Q2112552) (← links)
- Lift \& learn: physics-informed machine learning for large-scale nonlinear dynamical systems (Q2115511) (← links)
- Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers (Q2115516) (← links)
- Data-driven approximation of the Koopman generator: model reduction, system identification, and control (Q2115518) (← links)
- Poincaré maps for multiscale physics discovery and nonlinear Floquet theory (Q2115543) (← links)
- A data-driven approach for discovering stochastic dynamical systems with non-Gaussian Lévy noise (Q2115712) (← links)
- Bridging the gap: machine learning to resolve improperly modeled dynamics (Q2116291) (← links)
- Calibration of projection-based reduced-order models for unsteady compressible flows (Q2120780) (← links)
- Shortcuts to thermodynamic computing: the cost of fast and faithful information processing (Q2121410) (← links)
- Lagrangian dynamic mode decomposition for construction of reduced-order models of advection-dominated phenomena (Q2122715) (← links)
- Data-driven deep learning of partial differential equations in modal space (Q2123370) (← links)
- Non-intrusive reduced-order modeling using uncertainty-aware deep neural networks and proper orthogonal decomposition: application to flood modeling (Q2123910) (← links)
- Peridynamics enabled learning partial differential equations (Q2123993) (← links)
- Data-driven discovery of coarse-grained equations (Q2124010) (← links)
- A data-driven physics-informed finite-volume scheme for nonclassical undercompressive shocks (Q2124336) (← links)
- Symplectic neural networks in Taylor series form for Hamiltonian systems (Q2124341) (← links)
- On generalized residual network for deep learning of unknown dynamical systems (Q2124404) (← links)
- DLGA-PDE: discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm (Q2124551) (← links)
- Learning nonlinear turbulent dynamics from partial observations via analytically solvable conditional statistics (Q2124589) (← links)
- Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems (Q2125437) (← links)
- Transfer learning based multi-fidelity physics informed deep neural network (Q2127006) (← links)
- Structure-preserving neural networks (Q2127014) (← links)
- Data-driven discovery of emergent behaviors in collective dynamics (Q2127368) (← links)
- SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations (Q2128325) (← links)
- Learning non-Markovian physics from data (Q2128336) (← links)
- A data-driven, physics-informed framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings (Q2129328) (← links)
- Image inversion and uncertainty quantification for constitutive laws of pattern formation (Q2131064) (← links)
- Using neural networks to accelerate the solution of the Boltzmann equation (Q2132591) (← links)
- Weak SINDy for partial differential equations (Q2132599) (← links)
- System identification through Lipschitz regularized deep neural networks (Q2132640) (← links)
- Extended dynamic mode decomposition for inhomogeneous problems (Q2132641) (← links)
- Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data (Q2133022) (← links)
- Hybrid FEM-NN models: combining artificial neural networks with the finite element method (Q2133536) (← links)
- A robust framework for identification of PDEs from noisy data (Q2133542) (← links)
- Data-driven discovery of multiscale chemical reactions governed by the law of mass action (Q2134528) (← links)
- Machine learning moment closure models for the radiative transfer equation. I: Directly learning a gradient based closure (Q2135258) (← links)
- CFD-driven symbolic identification of algebraic Reynolds-stress models (Q2135797) (← links)
- Deep neural network modeling of unknown partial differential equations in nodal space (Q2136465) (← links)
- Revealing hidden dynamics from time-series data by ODENet (Q2138013) (← links)
- Learning biological dynamics from spatio-temporal data by Gaussian processes (Q2141319) (← links)
- Interpolatory tensorial reduced order models for parametric dynamical systems (Q2145123) (← links)
- tgEDMD: approximation of the Kolmogorov operator in tensor train format (Q2146443) (← links)
