Pages that link to "Item:Q2583396"

The following pages link to Equation-free, coarse-grained multiscale computation: enabling microscopic simulators to perform system-level analysis (Q2583396):

Displaying 27 items.

• An Elliptic Local Problem with Exponential Decay of the Resonance Error for Numerical Homogenization (Q6109134) (← links)
• Data-driven identification of parametric governing equations of dynamical systems using the signed cumulative distribution transform (Q6125481) (← links)
• Non-conforming multiscale finite element method for Stokes flows in heterogeneous media. II: Error estimates for periodic microstructure (Q6152152) (← links)
• Molecular dynamics simulations in hybrid particle-continuum schemes: pitfalls and caveats (Q6155127) (← links)
• Double diffusion maps and their latent harmonics for scientific computations in latent space (Q6158080) (← links)
• Parareal computation of stochastic differential equations with time-scale separation: a numerical convergence study (Q6163818) (← links)
• Human behavioral crowds review, critical analysis and research perspectives (Q6166564) (← links)
• Data-driven reduced order models using invariant foliations, manifolds and autoencoders (Q6168858) (← links)
• Deep neural network based adaptive learning for switched systems (Q6172098) (← links)
• Bayesian system ID: optimal management of parameter, model, and measurement uncertainty (Q6174350) (← links)
• Uniform error bounds for numerical schemes applied to multiscale SDEs in a Wong-Zakai diffusion approximation regime (Q6192520) (← links)
• Enabling equation-free modeling via diffusion maps (Q6196031) (← links)
• Data-driven modeling of partially observed biological systems (Q6537200) (← links)
• The identification of piecewise non-linear dynamical system without understanding the mechanism (Q6548691) (← links)
• Machine discovery of partial differential equations from spatiotemporal data: a sparse Bayesian learning framework (Q6553198) (← links)
• Using a library of chemical reactions to fit systems of ordinary differential equations to agent-based models: a machine learning approach (Q6559442) (← links)
• Wasserstein-penalized entropy closure: a use case for stochastic particle methods (Q6560686) (← links)
• Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics (Q6567586) (← links)
• The spatiotemporal coupling in delay-coordinates dynamic mode decomposition (Q6571520) (← links)
• Slow invariant manifolds of singularly perturbed systems via physics-informed machine learning (Q6573172) (← links)
• On the nature of the boundary resonance error in numerical homogenization and its reduction (Q6583628) (← links)
• Automated construction of effective potential via algorithmic implicit bias (Q6589866) (← links)
• Tipping points of evolving epidemiological networks: machine learning-assisted, data-driven effective modeling (Q6592553) (← links)
• An efficient numerical method for solving dynamical systems with multiple time scales (Q6616140) (← links)
• Learning macroscopic equations of motion from dissipative particle dynamics simulations of fluids (Q6641917) (← links)
• RandONets: shallow networks with random projections for learning linear and nonlinear operators (Q6648362) (← links)
• Slow invariant manifolds of fast-slow systems of ODEs with physics-informed neural networks (Q6661630) (← links)