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Surrogate Model Development for Digital Experiments in Welding
Published on: March 28, 2025
Robin Strickstrock1, Alexander Hagg1, Dirk Reith1,2
1Department of Engineering and Communication (DEC), Institute of Technology, Resource and Energy-efficient Engineering (TREE), Bonn-Rhein-Sieg University of Applied Sciences, 53757, Sankt Augustin, Germany.
机器学习模型通过取代缓慢的分子动力学模拟来显著加速力场参数的优化. 这种数据驱动的方法将计算时间缩短约20倍,同时保持高质量的力场用于分子建模.
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