.png){kind=icon}
返回
Guided experimental design for static nonparametric modeling
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ces.2024.120327.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Sequential or goal -oriented experimental design methods help achieve the target cheaply and quickly. In this work, we exploit Bayesian Optimization to obtain accurate and reliable static models for physical phenomena in cases where parametric models are unavailable. Our Guided Design of Experiments (G -DoE) workflow is proposed with a purely explorative acquisition function and an empirical stopping criterion. Two stopping criteria are proposed, one that is based on convergence rates of the prediction uncertainty and another that is based on a tolerance limit. Stopping criteria thresholds are established by studying factors like noise level and dimension size and their impact on prediction uncertainty convergence. Furthermore, we compare the G -DoE framework with classical Full Factorial and Response Surface strategies in terms of the suggested number of experiments and corresponding accuracy of fit. The proposed algorithm is tested using a methanol synthesis reaction experiment, a vapor pressure experiment, and a synthetic example.
Keyword:
Experimental design
Bayesian Optimization
Reaction rate modeling
Data-driven modeling
期刊
IF:
4.3
论文数:
2.3W
被引数:
5.5W
机构
引用论文
分享收藏Kounis Syndrome with Pericarditis Epistenocardica: a case reportKounis 综合征伴心包炎 Epistenocardica：一个病例报告.png)
OAAI
IF0
OAAI分享收藏Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design材料科学中的主动学习，重点是使用不确定性进行针对性设计的自适应采样
OAAI
OAAI分享收藏分享收藏分享收藏
查看更多内容