Pages that link to "Item:Q3763383"

The following pages link to Large Sample Properties of Simulations Using Latin Hypercube Sampling (Q3763383):

Displaying 50 items.

• Combining cross-entropy and MADS methods for inequality constrained global optimization (Q1981930) (← links)
• A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness (Q1987969) (← links)
• Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks (Q1989082) (← links)
• Emulating dynamic non-linear simulators using Gaussian processes (Q2002727) (← links)
• Latin hypercube sampling with inequality constraints (Q2006877) (← links)
• Bayesian model calibration and optimization of surfactant-polymer flooding (Q2009830) (← links)
• Efficient hierarchical surrogate-assisted differential evolution for high-dimensional expensive optimization (Q2053862) (← links)
• PINN deep learning method for the Chen-Lee-Liu equation: rogue wave on the periodic background (Q2060632) (← links)
• On the interface between nested designs and the multi-step interpolator (Q2063886) (← links)
• Data-driven discoveries of Bäcklund transformations and soliton evolution equations via deep neural network learning schemes (Q2081273) (← links)
• Data-driven solutions and parameter discovery of the Sasa-Satsuma equation via the physics-informed neural networks method (Q2083739) (← links)
• The distortion of the Peregrine soliton under the perturbation in initial condition (Q2093720) (← links)
• Representative points for distribution recovering (Q2112258) (← links)
• The nonlinear wave solutions and parameters discovery of the Lakshmanan-Porsezian-Daniel based on deep learning (Q2113140) (← links)
• The data-driven localized wave solutions of the derivative nonlinear Schrödinger equation by using improved PINN approach (Q2124077) (← links)
• Physics-informed neural networks for the shallow-water equations on the sphere (Q2133783) (← links)
• A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions (Q2135816) (← links)
• Multimodal information gain in Bayesian design of experiments (Q2135895) (← links)
• On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling (Q2136745) (← links)
• \(N\)-double poles solutions for nonlocal Hirota equation with nonzero boundary conditions using Riemann-Hilbert method and PINN algorithm (Q2140112) (← links)
• Mathematical modeling and projections of a vector-borne disease with optimal control strategies: a case study of the Chikungunya in Chad (Q2145540) (← links)
• An asymptotically compatible probabilistic collocation method for randomly heterogeneous nonlocal problems (Q2157079) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• RPINNs: rectified-physics informed neural networks for solving stationary partial differential equations (Q2166581) (← links)
• Data-driven rogue waves and parameters discovery in nearly integrable \(\mathcal{PT}\)-symmetric Gross-Pitaevskii equations via PINNs deep learning (Q2167994) (← links)
• Identification of input random field samples causing extreme responses (Q2174750) (← links)
• Multi-dimensional wavelet reduction for the homogenisation of microstructures (Q2175070) (← links)
• Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models (Q2179219) (← links)
• A bi-fidelity surrogate modeling approach for uncertainty propagation in three-dimensional hemodynamic simulations (Q2184449) (← links)
• A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet (Q2194450) (← links)
• A complete expected improvement criterion for Gaussian process assisted highly constrained expensive optimization (Q2200665) (← links)
• Uncertainty quantification in stability analysis of chaotic systems with discrete delays (Q2201356) (← links)
• Block-regularized repeated learning-testing for estimating generalization error (Q2201671) (← links)
• Dynamic simulation metamodeling using Mars: a case of radar simulation (Q2228788) (← links)
• Stratified Monte Carlo simulation of Markov chains (Q2229037) (← links)
• Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning (Q2233120) (← links)
• Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders (Q2234140) (← links)
• Model-data-driven constitutive responses: application to a multiscale computational framework (Q2234818) (← links)
• Variance reduction for sequential sampling in stochastic programming (Q2241206) (← links)
• Variance-based adaptive sequential sampling for polynomial chaos expansion (Q2246308) (← links)
• A hybrid sequential sampling strategy for sparse polynomial chaos expansion based on compressive sampling and Bayesian experimental design (Q2246331) (← links)
• Some large deviations results for Latin hypercube sampling (Q2276415) (← links)
• Approximating concept stability using variance reduction techniques (Q2286394) (← links)
• On stochastic linear systems with zonotopic support sets (Q2288639) (← links)
• Estimates of the coverage of parameter space by Latin hypercube and orthogonal array-based sampling (Q2295290) (← links)
• Stochastic reduced-order models for stable nonlinear ordinary differential equations (Q2296957) (← links)
• Stratified random sampling for dependent inputs in Monte Carlo simulations from computer experiments (Q2301061) (← links)
• Behavior characterization of visco-hyperelastic models for rubber-like materials using genetic algorithms (Q2307145) (← links)
• Reduced basis methods for nonlocal diffusion problems with random input data (Q2309052) (← links)
• Calibration experimental design considering field response and model uncertainty (Q2309083) (← links)