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Optimization and supervised machine learning methods for fitting numerical physics models without derivativesThe submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (‘Argonne’). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. http://energy.gov/downloads/doe-public-access-plan.
Journal of Physics G: Nuclear and Particle Physics ( IF 3.4 ) Pub Date : 2021-01-01 , DOI: 10.1088/1361-6471/abd009
Raghu Bollapragada 1, 2 , Matt Menickelly 1 , Witold Nazarewicz 3 , Jared O’Neal 1 , Paul-Gerhard Reinhard 4 , Stefan M Wild 1
Affiliation  

We address the calibration of a computationally expensive nuclear physics model for which derivative information with respect to the fit parameters is not readily available. Of particular interest is the performance of optimization-based training algorithms when dozens, rather than millions or more, of training data are available and when the expense of the model places limitations on the number of concurrent model evaluations that can be performed. As a case study, we consider the Fayans energy density functional model, which has characteristics similar to many model fitting and calibration problems in nuclear physics. We analyze hyperparameter tuning considerations and variability associated with stochastic optimization algorithms and illustrate considerations for tuning in different computational settings.



中文翻译:

无导数拟合数值物理模型的优化和监督机器学习方法提交的手稿是由UChicago Argonne,LLC(Argonne国家实验室(“ Argonne”)的运营商)创建的。美国能源部科学办公室实验室Argonne的运营合同编号为DE-AC02-06CH11357。美国政府在上述条款中为自己和代表其行事的其他人保留已付费的非排他性,不可撤销的全球许可,以复制或制作衍生作品,向公众分发副本,公开表演或公开展示或由他人或代表他人展示。政府的。能源部将根据DOE公共访问计划向公众提供这些联邦资助的研究结果。http://energy.gov/downloads/doe-public-access-plan

我们解决了一个计算上昂贵的核物理模型的校准问题,该模型的拟合参数派生信息不易获得。特别令人感兴趣的是,当有数十个而不是数百万个或更多的训练数据可用时,并且当模型的开销限制了可以执行的并发模型评估数量时,基于优化的训练算法的性能。作为案例研究,我们考虑了Fayans能量密度泛函模型,该模型具有与核物理中许多模型拟合和校准问题相似的特征。我们分析了与随机优化算法相关的超参数调整注意事项和可变性,并说明了在不同计算设置中进行调整的注意事项。

更新日期:2021-01-01
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