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.

• Data-driven discovery of governing equations for transient heat transfer analysis (Q2147572) (← links)
• Computing the invariant distribution of randomly perturbed dynamical systems using deep learning (Q2149015) (← links)
• Numerical bifurcation analysis of PDEs from lattice Boltzmann model simulations: a parsimonious machine learning approach (Q2149520) (← links)
• Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties (Q2160432) (← links)
• Retracted: Model order reduction method based on machine learning for parameterized time-dependent partial differential equations (Q2161825) (← links)
• Inadequacy of linear methods for minimal sensor placement and feature selection in nonlinear systems: a new approach using secants (Q2163754) (← links)
• Learning mean-field equations from particle data using WSINDy (Q2167982) (← links)
• A joint estimation approach to sparse additive ordinary differential equations (Q2172119) (← links)
• Variational system identification of the partial differential equations governing the physics of pattern-formation: inference under varying fidelity and noise (Q2173625) (← links)
• A few techniques to improve data-driven reduced-order simulations for unsteady flows (Q2176856) (← links)
• Assessment of end-to-end and sequential data-driven learning for non-intrusive modeling of fluid flows (Q2190672) (← links)
• On data-driven computation of information transfer for causal inference in discrete-time dynamical systems (Q2190698) (← links)
• Solving the inverse problem for an ordinary differential equation using conjugation (Q2194419) (← links)
• Learning equations from biological data with limited time samples (Q2202056) (← links)
• Learning physics by data for the motion of a sphere falling in a non-Newtonian fluid (Q2206129) (← links)
• Sparse linear regression from perturbed data (Q2208606) (← links)
• Recovery guarantees for polynomial coefficients from weakly dependent data with outliers (Q2209293) (← links)
• Methods to recover unknown processes in partial differential equations using data (Q2210652) (← links)
• Numerical aspects for approximating governing equations using data (Q2214649) (← links)
• Data-driven discovery of PDEs in complex datasets (Q2214651) (← links)
• From structured data to evolution linear partial differential equations (Q2222252) (← links)
• Identification of physical processes via combined data-driven and data-assimilation methods (Q2222261) (← links)
• Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning (Q2222332) (← links)
• Data driven governing equations approximation using deep neural networks (Q2222362) (← links)
• Deep learning of dynamics and signal-noise decomposition with time-stepping constraints (Q2222431) (← links)
• Sparse identification of truncation errors (Q2222522) (← links)
• Machine learning for fast and reliable solution of time-dependent differential equations (Q2222523) (← links)
• PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network (Q2222627) (← links)
• Data-driven, variational model reduction of high-dimensional reaction networks (Q2222683) (← links)
• Extraction and prediction of coherent patterns in incompressible flows through space-time koopman analysis (Q2222732) (← links)
• Adaptive tuning of network traffic policing mechanisms for DDoS attack mitigation systems (Q2235486) (← links)
• Hidden physics model for parameter estimation of elastic wave equations (Q2236961) (← links)
• Data-driven identification of 2D partial differential equations using extracted physical features (Q2236988) (← links)
• Unsupervised discovery of interpretable hyperelastic constitutive laws (Q2237006) (← links)
• Analytical mechanics allows novel vistas on mathematical epidemic dynamics modeling (Q2245656) (← links)
• Parametric deep energy approach for elasticity accounting for strain gradient effects (Q2246296) (← links)
• Data-driven closure of projection-based reduced order models for unsteady compressible flows (Q2246325) (← links)
• Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction (Q2281470) (← links)
• Neural machine-based forecasting of chaotic dynamics (Q2296659) (← links)
• Using extremal events to characterize noisy time series (Q2303748) (← links)
• Variational approach for learning Markov processes from time series data (Q2303757) (← links)
• Commutation error in reduced order modeling of fluid flows (Q2305543) (← links)
• Reconstruction of ensembles of nonlinear neurooscillators with sigmoid coupling function (Q2308134) (← links)
• Data-driven operator inference for nonintrusive projection-based model reduction (Q2309194) (← links)
• A physics-constrained data-driven approach based on locally convex reconstruction for noisy database (Q2309342) (← links)
• Deep learning algorithm for data-driven simulation of noisy dynamical system (Q2311511) (← links)
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations (Q2314336) (← links)
• Learning slosh dynamics by means of data (Q2319406) (← links)
• Sparse feature map-based Markov models for nonlinear fluid flows (Q2331856) (← links)
• Data-driven surrogate modeling of multiphase flows using machine learning techniques (Q2664065) (← links)