A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
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DOI：
10.5555/1756006.1859919
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发表时间：
2010-03
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影响因子：
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通讯作者：
D. Gorissen;I. Couckuyt;P. Demeester;T. Dhaene;K. Crombecq
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文献类型：
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作者：
D. Gorissen;I. Couckuyt;P. Demeester;T. Dhaene;K. Crombecq
大量的科学和工程领域都需要计算机模拟来研究复杂的真实的世界现象或解决具有挑战性的设计问题。然而，由于这些高保真仿真的计算成本，神经网络，核方法和其他替代建模技术的使用已成为不可或缺的。代理模型是紧凑和廉价的评估，并已被证明是非常有用的任务，如优化，设计空间探索，原型和灵敏度分析。因此，在许多领域中，人们对有助于构建这种回归模型的工具和技术非常感兴趣，同时使计算成本最小化并使模型精度最大化。本文提出了一个成熟、灵活、自适应的机器学习工具包，用于回归建模和主动学习，以解决这些问题。该工具包汇集了数据拟合，模型选择，样本选择（主动学习），超参数优化和分布式计算的算法，以使领域专家能够有效地为手头的问题或数据生成准确的模型。
An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging design problems. However, due to the computational cost of these high fidelity simulations, the use of neural networks, kernel methods, and other surrogate modeling techniques have become indispensable. Surrogate models are compact and cheap to evaluate, and have proven very useful for tasks such as optimization, design space exploration, prototyping, and sensitivity analysis. Consequently, in many fields there is great interest in tools and techniques that facilitate the construction of such regression models, while minimizing the computational cost and maximizing model accuracy. This paper presents a mature, flexible, and adaptive machine learning toolkit for regression modeling and active learning to tackle these issues. The toolkit brings together algorithms for data fitting, model selection, sample selection (active learning), hyperparameter optimization, and distributed computing in order to empower a domain expert to efficiently generate an accurate model for the problem or data at hand.