Miroslav Vorechovský

Orcid: 0000-0002-3366-5557

According to our database1, Miroslav Vorechovský authored at least 16 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Stratified sample tiling.
Adv. Eng. Softw., 2024

2023
Failure Probability Estimation and Detection of Failure Surfaces via Adaptive Sequential Decomposition of the Design Domain.
CoRR, 2023

Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion.
CoRR, 2023

2022
Reliability analysis of discrete-state performance functions via adaptive sequential sampling with detection of failure surfaces.
CoRR, 2022

2021
Bone mineral density modeling via random field: Normality, stationarity, sex and age dependence.
Comput. Methods Programs Biomed., 2021

2020
Modification of the Maximin and ϕp (Phi) Criteria to Achieve Statistically Uniform Distribution of Sampling Points.
Technometrics, 2020

Fracture in random heterogeneous media: I. Discrete mesoscale simulations of load capacity and active zone.
CoRR, 2020

Distance-based optimal sampling in a hypercube: Energy potentials for high-dimensional and low-saturation designs.
Adv. Eng. Softw., 2020

Periodic version of the minimax distance criterion for Monte Carlo integration.
Adv. Eng. Softw., 2020

2019
Distance-based optimal sampling in a hypercube: Analogies to N-body systems.
Adv. Eng. Softw., 2019

Approximation of Voronoï cell attributes using parallel solution.
Adv. Eng. Softw., 2019

2018
Parallel implementation of hyper-dimensional dynamical particle system on CUDA.
Adv. Eng. Softw., 2018

2016
Modification of the Audze-Eglājs criterion to achieve a uniform distribution of sampling points.
Adv. Eng. Softw., 2016

2015
Hierarchical Refinement of Latin Hypercube Samples.
Comput. Aided Civ. Infrastructure Eng., 2015

2014
FReET: Software for the statistical and reliability analysis of engineering problems and FReET-D: Degradation module.
Adv. Eng. Softw., 2014

2013
Using Python for scientific computing: Efficient and flexible evaluation of the statistical characteristics of functions with multivariate random inputs.
Comput. Phys. Commun., 2013


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