Results 31 to 40 of about 289,279 (277)

An interpretable machine learning model for predicting cavity water depth and cavity length based on XGBoost–SHAP

Journal of Hydroinformatics, 2023
In contrast to the traditional black box machine learning model, the white box model can achieve higher prediction accuracy and accurately evaluate and explain the prediction results.
Tiexiang Mo, Shanshan Li, Guodong Li
doaj   +1 more source

Asynchronous batch Bayesian optimization with pipelining evaluations in experimental equipment-limited situations

SLAS Technology
Bayesian optimization is efficient even with a small amount of data and is used in engineering and in science, including biology and chemistry. In Bayesian optimization, a parameterized model with an uncertainty is fitted to explain the experimental data,
Yujin Taguchi, Yusuke Shibuya, Yusuke Hiki, Takashi Morikura, Takahiro G. Yamada, Akira Funahashi   +5 more
doaj   +1 more source

Hybrid Optimization Algorithm for Bayesian Network Structure Learning

Information, 2019
Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research.
Xingping Sun, Chang Chen, Lu Wang, Hongwei Kang, Yong Shen, Qingyi Chen   +5 more
doaj   +1 more source

Deep-Learning-Guided High-Throughput Evaluation of Ligands for Selective Sr/Cs Coordination

He huaxue yu fangshe huaxue, 2023
From a coordination chemistry perspective, we aimed to advance the knowledge of Sr/Cs separation in the scheme of spent nuclear fuel reprocessing. Based on data mining of crystal structures and deep learning architecture, we summarized and analyzed ...
ZHANG Zhi-yuan, DONG Yue, QIU Yu-qing, BI Ke-xin, HU Kong-qiu, DAI Yi-yang, ZHOU Li, LIU Chong, JI Xu, SHI Wei-qun   +9 more
doaj   +1 more source

Benchmarking five global optimization approaches for nano-optical shape optimization and parameter reconstruction

, 2019
Numerical optimization is an important tool in the field of computational physics in general and in nano-optics in specific. It has attracted attention with the increase in complexity of structures that can be realized with nowadays nano-fabrication ...
Burger, Sven, Hammerschmidt, Martin, Rockstuhl, Carsten, Santiago, Xavier Garcia, Schneider, Philipp-Immanuel, Soltwisch, Victor   +5 more
core   +1 more source

Bayesian confidence in optimal decisions

Psychological Review, 2020
The optimal way to make decisions in many circumstances is to track the difference in evidence collected in favour of the options. The drift diffusion model (DDM) implements this approach, and provides an excellent account of decisions and response times.
Joshua Calder-Travis, Lucie Charles, Rafal Bogacz, Nick Yeung   +3 more
openaire   +5 more sources

Accurate and reliable estimation of kinetic parameters for environmental engineering applications: A global, multi objective, Bayesian optimization approach

MethodsX, 2019
Accurate and reliable predictions of bacterial growth and metabolism from unstructured kinetic models are critical to the proper operation and design of engineered biological treatment and remediation systems. As such, parameter estimation has progressed
Derek C. Manheim, Russell L. Detwiler
doaj   +1 more source

Exploratory Landscape Validation for Bayesian Optimization Algorithms

Mathematics
Bayesian optimization algorithms are widely used for solving problems with a high computational complexity in terms of objective function evaluation. The efficiency of Bayesian optimization is strongly dependent on the quality of the surrogate models of ...
Taleh Agasiev, Anatoly Karpenko
doaj   +1 more source

Bayesian optimization for materials design

, 2015
We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets.
A Booker, A Forrester, AB Gelman, AIJ Forrester, B Ankenman, BE Stuckman, CE Rasmussen, D Huang, D Huang, David Ginsbourger, Diana M. Negoescu, DR Jones, HJ Kushner, J Bect, J Knowles, J Mockus, J Villemonteix, J Xie, LP Kaelbling, Noel Cressie, PI Frazier, PI Frazier, PI Frazier, R Waeber, RA Howard, RS Sutton, Sethuraman Sankaran, TJ Santner, W Scott   +28 more
core   +1 more source

Simulation based Bayesian Optimization

Statistics and Computing
Bayesian Optimization (BO) is a powerful method for optimizing black-box functions by combining prior knowledge with ongoing function evaluations. BO constructs a probabilistic surrogate model of the objective function given the covariates, which is in turn used to inform the selection of future evaluation points through an acquisition function.
Naveiro, Roi, Tang, Becky
openaire   +2 more sources