Multi-information source constrained Bayesian optimization

Ghoreishi, Seyede Fatemeh; Allaire, Douglas

doi:10.1007/s00158-018-2115-z

Cited by 51 publications

(14 citation statements)

References 32 publications

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“…The collective error was included with Gaussian distributions, and the methodology was investigated using synthetic data along with its applications (Zhang et al 2020). Bayesian optimisation was also previously used in a constrained optimisation problem with multiple available sources (Ghoreishi and Allaire 2019).…”

Section: Background and Motivationmentioning

confidence: 99%

Model updating using causal information: a case study in coupled slab

Tiwary

Patro

Gandomi

et al. 2022

Struct Multidisc Optim

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Problems like improper sampling (sampling on unnecessary variables) and undefined prior distribution (or taking random priors) often occur in model updating. Any such limitations on model parameters can lead to lower accuracy and higher experimental costs (due to more iterations) of structural optimisation. In this work, we explored the effective dimensionality of the model updating problem by leveraging the causal information. In order to utilise the causal structure between the parameters, we used Causal Bayesian Optimisation (CBO), a recent variant of Bayesian Optimisation, to integrate observational and intervention data. We also employed generative models to generate synthetic observational data, which helps in creating a better prior for surrogate models. This case study of a coupled slab structure in a recreational building resulted in the modal updated frequencies which were extracted from the finite element of the structure and compared to measured frequencies from ambient vibration tests found in the literature. The results of mode shapes between experimental and predicted values were also compared using modal assurance criterion (MAC) percentages. The updated frequency and MAC number that was obtained using the proposed model was found in least number of iterations (impacts experimental budget) as compared to previous approaches which optimise the same parameters using same data. This also shows how the causal information has impact on experimental budget.

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Section: Background and Motivationmentioning

confidence: 99%

Model updating using causal information: a case study in coupled slab

Tiwary

Patro

Gandomi

et al. 2022

Struct Multidisc Optim

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show abstract

“…Last, but not least, the fidelity level of the data is important, as it defines the one more level of complexity in the surrogate modeling. Multi-fidelity approaches are available and detailed in [28,70,229].…”

Section: A Practical View On Sequential Samplingmentioning

confidence: 99%

Surrogate modeling: tricks that endured the test of time and some recent developments

Viana

Gogu

Goel³

2021

Struct Multidisc Optim

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Tasks such as analysis, design optimization, and uncertainty quantification can be computationally expensive. Surrogate modeling is often the tool of choice for reducing the burden associated with such data-intensive tasks. However, even after years of intensive research, surrogate modeling still involves a struggle to achieve maximum accuracy within limited resources. This work summarizes various advanced, yet often straightforward, statistical tools that help. We focus on four techniques with increasing popularity in the surrogate modeling community: (i) variable screening and dimensionality reduction in both the input and the output spaces, (ii) data sampling techniques or design of experiments, (iii) simultaneous use of multiple surrogates, and (iv) sequential sampling. We close the paper with some suggestions for future research.

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“…In (Lam et al, 2015) the first approach for nonhierarchical information sources has been proposed, addressing location-dependent fidelities of the sources and defining the more general MISO setting. More recently, (Poloczek et al, 2017;Ghoreishi and Allaire, 2019) have been provided improvements to (Lam et al, 2015). All these methods are based on the idea of using a separate model for each information source (i.e., a Gaussian Process -GP) and then fusing their predictions and related uncertainties through the method proposed in (Winkler, 1981), which became the standard practice for the fusion of normally distributed data.…”

Section: Multi-information Source Optimizationmentioning

confidence: 99%

MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costs

Candelieri,

Archetti

2021

Preprint

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This paper addresses black-box optimization over multiple information sources whose both fidelity and query cost change over the search space, that is they are location dependent. The approach uses: (i) an Augmented Gaussian Process, recently proposed in multi-information source optimization as a single model of the objective function over search space and sources, and (ii) a Gaussian Process to model the location-dependent cost of each source. The former is used into a Confidence Bound based acquisition function to select the next source and location to query, while the latter is used to penalize the value of the acquisition depending on the expected query cost for any source-location pair. The proposed approach is evaluated on a set of Hyperparameters Optimization tasks, consisting of two Machine Learning classifiers and three datasets of different sizes.

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Multi-information source constrained Bayesian optimization

Cited by 51 publications

References 32 publications

Model updating using causal information: a case study in coupled slab

Model updating using causal information: a case study in coupled slab

Surrogate modeling: tricks that endured the test of time and some recent developments

MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costs

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