新策略方法将黑盒模拟器调用次数减少了 10 倍

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员开发了一种优化黑盒模拟器的新方法，这些模拟器通常不可微分且计算成本高昂。该方法涉及学习一个策略来指导可微分代理模型的训练。然后，利用该代理模型的梯度通过 SGD 优化模拟参数。该技术分摊了下游优化的成本，与现有的贝叶斯优化和数值优化等方法相比，所需的模拟器调用次数减少了 10 倍。 AI

影响该方法可以显著降低训练和优化复杂模拟模型的计算成本，从而加速各个科学和工程领域的研发。

排序理由该集群包含一篇详细介绍黑盒优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Tim Bakker, Fabio Valerio Massoli, Thomas Hehn, Tribhuvanesh Orekondy, Arash Behboodi · 2026-10-21 04:00

模拟，快与慢：为黑盒优化学习策略

arXiv:2406.04261v2 Announce Type: replace Abstract: Simulators are vital in science and engineering, as they faithfully model the influence of design parameters on real-world observations. Optimizing design parameters to minimize a desired objective function is notoriously challe…