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.
- SPADE4: sparsity and delay embedding based forecasting of epidemics (Q6168035) (← links)
- Data-driven reduced order models using invariant foliations, manifolds and autoencoders (Q6168858) (← links)
- Reduced-order variational mode decomposition to reveal transient and non-stationary dynamics in fluid flows (Q6169298) (← links)
- Detecting Stability and Bifurcation Points in Control-Based Continuation for a Physical Experiment of the Zeeman Catastrophe Machine (Q6171200) (← links)
- Koopman Operator Inspired Nonlinear System Identification (Q6171206) (← links)
- Physics informed and data-based augmented learning in structural health diagnosis (Q6171237) (← links)
- Deep neural network based adaptive learning for switched systems (Q6172098) (← links)
- Divide and Conquer: A Quick Scheme for Symbolic Regression (Q6172979) (← links)
- Bayesian system ID: optimal management of parameter, model, and measurement uncertainty (Q6174350) (← links)
- Evolutionary sparse data-driven discovery of multibody system dynamics (Q6175006) (← links)
- Nonintrusive Reduced-Order Models for Parametric Partial Differential Equations via Data-Driven Operator Inference (Q6175122) (← links)
- Discovering efficient periodic behaviors in mechanical systems via neural approximators (Q6180273) (← links)
- Towards effective information content assessment: analytical derivation of information loss in the reconstruction of random fields with model uncertainty (Q6180610) (← links)
- Rigorous data‐driven computation of spectral properties of Koopman operators for dynamical systems (Q6180710) (← links)
- Nonparametric inference of stochastic differential equations based on the relative entropy rate (Q6182259) (← links)
- Learning stiff chemical kinetics using extended deep neural operators (Q6185234) (← links)
- Bayesian deep learning for partial differential equation parameter discovery with sparse and noisy data (Q6186272) (← links)
- Data-driven model identification using forcing-induced limit cycles (Q6191513) (← links)
- Ensemble forecasts in reproducing kernel Hilbert space family (Q6191535) (← links)
- Identifying stochastic governing equations from data of the most probable transition trajectories (Q6191971) (← links)
- Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects (Q6192109) (← links)
- Learning the Dynamics for Unknown Hyperbolic Conservation Laws Using Deep Neural Networks (Q6195013) (← links)
- Bridging scales: a hybrid model to simulate vascular tumor growth and treatment response (Q6201151) (← links)
- Piecewise DMD for oscillatory and Turing spatio-temporal dynamics (Q6202634) (← links)
- Towards discovery of the differential equations (Q6204271) (← links)
- Self-tuning model predictive control for wake flows (Q6204881) (← links)
- PDE-READ: human-readable partial differential equation discovery using deep learning (Q6488684) (← links)
- Preserving bifurcations through moment closures (Q6492263) (← links)
- Convergence of weak-SINDy surrogate models (Q6492266) (← links)
- $$\mathcal {H}_2$$ optimal rational approximation on general domains (Q6495876) (← links)
- Koopman-based surrogate models for multi-objective optimization of agent-based systems (Q6496460) (← links)
- Learning dynamical systems from data: a simple cross-validation perspective. V: Sparse kernel flows for 132 chaotic dynamical systems (Q6496480) (← links)
- A kernel framework for learning differential equations and their solution operators (Q6496499) (← links)
- Correcting model misspecification in physics-informed neural networks (PINNs) (Q6497270) (← links)
- \texttt{Weak-PDE-LEARN}: a weak form based approach to discovering PDEs from noisy, limited data (Q6498485) (← links)
- Latent assimilation with implicit neural representations for unknown dynamics (Q6498490) (← links)
- Dictionary-based model reduction for state estimation (Q6500167) (← links)
- Bridge successive states for a complex system with evolutionary matrix (Q6500374) (← links)
- A proximal alternating direction method of multipliers for DC programming with structured constraints (Q6536834) (← links)
- A mathematical framework for evo-devo dynamics (Q6546682) (← links)
- A data-driven framework for learning hybrid dynamical systems (Q6548679) (← links)
- The identification of piecewise non-linear dynamical system without understanding the mechanism (Q6548691) (← links)
- Data-informed reservoir computing for efficient time-series prediction (Q6549981) (← links)
- Dynamical and statistical properties of estimated high-dimensional ODE models: the case of the Lorenz '05 type II model (Q6549999) (← 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)
- Symbolic regression via neural networks (Q6550761) (← links)
- Interpretable polynomial neural ordinary differential equations (Q6551380) (← links)
- Data-driven modeling and forecasting of chaotic dynamics on inertial manifolds constructed as spectral submanifolds (Q6552138) (← links)
- Machine discovery of partial differential equations from spatiotemporal data: a sparse Bayesian learning framework (Q6553198) (← links)
