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使用数据驱动优化来自动化动力蒙特卡洛模型的参数化

Ioannis Kouroudis1, Manuel Gößwein1, Alessio Gagliardi1

  • 1Department of Electrical and Computer Engineering, Technical University of Munich, Hans-Piloty-Strasse 1/III, 85748 Garching bei München, Germany.

The journal of physical chemistry. A
|July 8, 2023
PubMed
概括

本研究介绍了一种基于数据的方法,使用高斯过程和贝叶斯优化来有效参数化动力蒙特卡洛 (kMC) 模拟. 这种方法显著降低了计算成本,并加速了对复杂系统的合适输入参数的发现.

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