Youssef M. Marzouk | DeepAI

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Multilevel Monte Carlo estimators for derivative-free optimization under uncertainty

Optimization is a key tool for scientific and engineering applications, ...

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∙ 02/27/2023

An Approximation Theory Framework for Measure-Transport Sampling Algorithms

This article presents a general approximation-theoretic framework to ana...

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∙ 02/14/2023

Transport map unadjusted Langevin algorithms

Langevin dynamics are widely used in sampling high-dimensional, non-Gaus...

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∙ 06/22/2022

Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport

We consider the Bayesian calibration of models describing the phenomenon...

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∙ 10/07/2021

Computing eigenfunctions of the multidimensional Ornstein-Uhlenbeck operator

We discuss approaches to computing eigenfunctions of the Ornstein–Uhlenb...

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∙ 08/18/2021

Geometry-informed irreversible perturbations for accelerated convergence of Langevin dynamics

We introduce a novel geometry-informed irreversible perturbation that ac...

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∙ 06/09/2020

Cross-entropy-based importance sampling with failure-informed dimension reduction for rare event simulation

The estimation of rare event or failure probabilities in high dimensions...

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∙ 06/24/2019

Bayesian waveform-based calibration of high-pressure acoustic emission systems with ball drop measurements

Acoustic emission (AE) is a widely used technology to study source mecha...

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∙ 03/26/2019

A layered multiple importance sampling scheme for focused optimal Bayesian experimental design

We develop a new computational approach for "focused" optimal Bayesian e...

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∙ 04/28/2016

Sequential Bayesian optimal experimental design via approximate dynamic programming

The design of multiple experiments is commonly undertaken via suboptimal...

0 Xun Huan, et al. ∙

∙ 08/20/2011

Simulation-based optimal Bayesian experimental design for nonlinear systems

The optimal selection of experimental conditions is essential to maximiz...

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