Marcos Valdebenito

Chief Engineer

Technische Universität Dortmund

Germany

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About

Marcos Valdebenito is a Chief Engineer at TU Dortmund University in Germany. His research focuses on probability sensitivity estimation, reliability analysis, and uncertainty propagation methods, as highlighted in his recent articles. He employs Bayesian techniques and Gaussian process regression to enhance understanding in these areas. Additionally, he has contributed to advancements in seepage analysis and interval isogeometric analysis.

Articles (20)

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2025

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2023

Collaborators (4)

Matthias Faes

Professor (full)

Technische Universität Dortmund

GERMANY

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Jan Liedmann

Jun.-Prof.

University of Stuttgart

GERMANY

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Michael Beer

GERMANY

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Vasileios Fragkoulis

Lecturer

University of Liverpool

UNITED KINGDOM

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Marcos Valdebenito

Research Interests

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Articles (20)

Probability Sensitivity Estimation with Respect to Distribution Parameters via the Method of Moments

A Bayesian piecewise fitting method for estimating probability distributions of performance functions

Time-dependent reliability analysis by a single-loop Bayesian active learning method using Gaussian process regression

First excursion probability sensitivity in stochastic linear dynamics by means of Domain Decomposition Method

Avoiding two common pitfalls in uncertainty propagation

Response probability distribution estimation of expensive computer simulators: A Bayesian active learning perspective using Gaussian process regression

Confined seepage analysis of saturated soils using fuzzy fields

Interval Isogeometric Analysis for coping with geometric uncertainty

Towards a single-loop Gaussian process regression based-active learning method for time-dependent reliability analysis

Directional importance sampling for dynamic reliability of linear structures under non-Gaussian white noise excitation

An approximate decoupled reliability-based design optimization method for efficient design exploration of linear structures under random loads

Design Optimization with Variable Screening by Interval-Based Sensitivity Analysis

Bayesian active learning line sampling with log-normal process for rare-event probability estimation

Control variates with splitting for aggregating results of Monte Carlo simulation and perturbation analysis

Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments

Line sampling for time-variant failure probability estimation using an adaptive combination approach

Partially Bayesian active learning cubature for structural reliability analysis with extremely small failure probabilities

Operator norm-based determination of failure probability of nonlinear oscillators with fractional derivative elements subject to imprecise stationary Gaussian loads

Constrained Bayesian optimization algorithms for estimating design points in structural reliability analysis

Collaborative and Adaptive Bayesian Optimization for bounding variances and probabilities under hybrid uncertainties

Collaborators (4)

Matthias Faes

Jan Liedmann

Michael Beer

Vasileios Fragkoulis

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