England after the Conquest

Garnett, George

doi:10.1017/9781009277105.003

Cited by 1 publication

(2 citation statements)

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“…The performance of RGPE‐TAF relative to RGPE‐NEI is contrary to what has been observed by Feurer et al., where RGPE‐TAF outperformed RGPE‐NEI by a small margin. [ 23 ] We hypothesize that the performance of a BO algorithm varies according to the domain it is applied to, which is expected as the behavior of defined acquisition functions and statistical models differs, [ 19 ] this aspect is further discussed later in the text.…”

Section: Resultsmentioning

confidence: 99%

“…[ 17,18 ] The BO algorithms are distinguished by the choice of the statistical model and acquisition function, and the performance depends on how these two components are implemented, the main challenge in BO research. An extensive discussion of BO, from its basic implementation to its application across various domains, can be found in the text by Garnett R. [ 19 ] Some other sources providing overviews and advances in BO can be found in the works by Frazier, Shahriari et al., and Binois et al. [ 18,20–22 ] Of particular interest is the work of Feurer et al., [ 23 ] where authors introduce a method for improving the efficiency of BO algorithms by building base models from the information of previous similar tasks (prior knowledge) and incorporating them into optimizing new (target) task.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Optimization with Experience for Fast Development of Monolithic Tandem Solar Cells: Simulation Case Study

Tsoi,

Yerci

2024

Advcd Theory and Sims

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Machine learning (ML) is gaining more attention in photovoltaic research and will be a vital tool in reaching record‐high power conversion efficiencies (PCE) in the near future. One area, where ML is significantly beneficial is reducing the number of experiments needed to find the optimum combination of parameters in solar cell fabrication. Bayesian optimization (BO) provides routes for quickly identifying optimum parameters in problems with large parameter space. In this work, BO algorithms utilizing previous knowledge are demonstrated to result in faster optimization of tandem solar cells, a technology rapidly gaining more interest due to its potential to deliver record‐high performance. Namely, it is shown that in the space of all possible parameter combinations that take ≈88 years to evaluate, optimum PCE can be obtained in 20 min. Moreover, it is demonstrated that methods utilizing previous knowledge outperform those that do not by yielding an increase of ≈8.9%abs. in the 1st iteration and requiring 5× less time to reach a target PCE of 38.5% across 20 different trials. Results from this work help accelerate the development of tandem solar cells by removing the need for large numbers of experiments in identifying optimum parameters.

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Section: Resultsmentioning

confidence: 99%