A supermartingale approach to Gaussian process based sequential design of experiments
Gaussian process (GP) models have become a well-established framework for the adaptive
design of costly experiments, and notably of computer experiments. GP-based sequential …
design of costly experiments, and notably of computer experiments. GP-based sequential …
Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations
Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering
Pareto fronts or Pareto sets from a limited number of function evaluations are challenging …
Pareto fronts or Pareto sets from a limited number of function evaluations are challenging …
Reliability analysis and optimal design under uncertainty-Focus on adaptive surrogate-based approaches
JM Bourinet - 2018 - theses.hal.science
The design of optimal and reliable systems is an objective which is pursued in several fields
of engineering. Optimality is expressed in terms of a system cost which needs to be …
of engineering. Optimality is expressed in terms of a system cost which needs to be …
Differentiating the multipoint expected improvement for optimal batch design
This work deals with parallel optimization of expensive objective functions which are
modelled as sample realizations of Gaussian processes. The study is formalized as a …
modelled as sample realizations of Gaussian processes. The study is formalized as a …
Adaptive design of experiments for conservative estimation of excursion sets
We consider the problem of estimating the set of all inputs that leads a system to some
particular behavior. The system is modeled by an expensive-to-evaluate function, such as a …
particular behavior. The system is modeled by an expensive-to-evaluate function, such as a …
Multi-robot learning and coverage of unknown spatial fields
This paper addresses the problem of optimally covering a domain when the scalar function
that describes the relative importance of the points in the domain is initially unknown. We …
that describes the relative importance of the points in the domain is initially unknown. We …
Rare Event Detection by Acquisition-Guided Sampling
Motivated by the challenges in detecting extremely rare failures for sophisticated
specifications in circuit design, we consider the problem of detecting regions of interest …
specifications in circuit design, we consider the problem of detecting regions of interest …
A new expected-improvement algorithm for continuous minimax optimization
Worst-case design is important whenever robustness to adverse environmental conditions
must be ensured regardless of their probability. It leads to minimax optimization, which is …
must be ensured regardless of their probability. It leads to minimax optimization, which is …
A sampling criterion for constrained Bayesian optimization with uncertainties
We consider the problem of chance constrained optimization where it is sought to optimize a
function and satisfy constraints, both of which are affected by uncertainties. The real world …
function and satisfy constraints, both of which are affected by uncertainties. The real world …
Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation
We consider the problem of learning the level set for which a noisy black-box function
exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian …
exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian …