Pages that link to "Item:Q137310"

The following pages link to Discovering governing equations from data by sparse identification of nonlinear dynamical systems (Q137310):

Displaying 50 items.

• Adaptive integral alternating minimization method for robust learning of nonlinear dynamical systems from highly corrupted data (Q6553634) (← links)
• Learning nonparametric ordinary differential equations from noisy data (Q6553789) (← links)
• Data-driven models of nonautonomous systems (Q6553794) (← links)
• A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty (Q6553801) (← links)
• Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale dynamical systems (Q6554903) (← links)
• Simplicity bias, algorithmic probability, and the random logistic map (Q6554925) (← links)
• Inferring bifurcation diagrams with transformers (Q6555010) (← links)
• Identification of network interactions from time series data: an iterative approach (Q6555020) (← links)
• The occupation kernel method for nonlinear system identification (Q6555693) (← links)
• Finding nonlinear system equations and complex network structures from data: a sparse optimization approach (Q6556901) (← links)
• Noise robust approach to reconstruction of van der Pol-like oscillators and its application to Granger causality (Q6556923) (← links)
• Stiff neural ordinary differential equations (Q6556966) (← links)
• Analysis of chaotic dynamical systems with autoencoders (Q6557712) (← links)
• Model-free inference of unseen attractors: reconstructing phase space features from a single noisy trajectory using Reservoir computing (Q6557728) (← links)
• Data-driven discovery of interpretable Lagrangian of stochastically excited dynamical systems (Q6557792) (← links)
• Train small, model big: scalable physics simulators via reduced order modeling and domain decomposition (Q6557808) (← links)
• Hausdorff metric based training of kernels to learn attractors with application to 133 chaotic dynamical systems (Q6558876) (← links)
• Reconstructing dynamics of complex systems from noisy time series with hidden variables (Q6559218) (← links)
• Using a library of chemical reactions to fit systems of ordinary differential equations to agent-based models: a machine learning approach (Q6559442) (← links)
• Deep learning enhanced dynamic mode decomposition (Q6560590) (← links)
• Learning the temporal evolution of multivariate densities via normalizing flows (Q6560595) (← links)
• Data driven adaptive Gaussian mixture model for solving Fokker-Planck equation (Q6560605) (← links)
• Sparse identification of nonlinear dynamical systems via non-convex penalty least squares (Q6561186) (← links)
• Knowledge-based learning of nonlinear dynamics and chaos (Q6562228) (← links)
• Entropic regression with neurologically motivated applications (Q6562237) (← links)
• Learning particle swarming models from data with Gaussian processes (Q6562843) (← links)
• Conditional Gaussian nonlinear system: a fast preconditioner and a cheap surrogate model for complex nonlinear systems (Q6563632) (← links)
• Data-driven reduced-order modeling of spatiotemporal chaos with neural ordinary differential equations (Q6565124) (← links)
• Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: a chaotic Kuramoto-Sivashinsky test case (Q6565142) (← links)
• Model selection of chaotic systems from data with hidden variables using sparse data assimilation (Q6565144) (← links)
• An end-to-end deep learning approach for extracting stochastic dynamical systems with \(\alpha\)-stable Lévy noise (Q6565156) (← links)
• Automated model discovery for human cardiac tissue: discovering the best model and parameters (Q6566049) (← links)
• On the sample complexity of stabilizing linear dynamical systems from data (Q6566152) (← links)
• Gabor-filtered Fourier neural operator for solving partial differential equations (Q6566939) (← links)
• Stability preserving data-driven models with latent dynamics (Q6567566) (← links)
• Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics (Q6567586) (← links)
• Constructing differential equations using only a scalar time-series about continuous time chaotic dynamics (Q6569952) (← links)
• Identifying causality drivers and deriving governing equations of nonlinear complex systems (Q6570776) (← links)
• Learning chaotic systems from noisy data via multi-step optimization and adaptive training (Q6571528) (← links)
• Regularized least absolute deviation-based sparse identification of dynamical systems (Q6571784) (← links)
• Learning about structural errors in models of complex dynamical systems (Q6572173) (← links)
• Structural inference of networked dynamical systems with universal differential equations (Q6572656) (← links)
• Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning (Q6572673) (← links)
• Data augmentation-based statistical inference of diffusion processes (Q6573480) (← links)
• Data-driven identification of the spectral operator in AKNS Lax pairs using conserved quantities (Q6573517) (← links)
• An adaptive, training-free reduced-order model for convection-dominated problems based on hybrid snapshots (Q6574153) (← links)
• A dynamical system-based framework for dimension reduction (Q6575279) (← links)
• Inferring dynamical models from time-series biological data using an interpretable machine learning method based on weighted expression trees (Q6581204) (← links)
• Dynamics-augmented cluster-based network model (Q6582862) (← links)
• When data driven reduced order modeling meets full waveform inversion (Q6585280) (← links)