报告时间：2025年9月29日上午10:00 (10:00, Sept.29, 2025)

报告地点：物质科研楼A309会议室 Room A309 Material Science Building)

报告人：  韦业助理教授 香港城市大学

报告题目/Title:Data-driven optimization for complex systems

摘要/Abstract:

Inferring optimal solutions from limited data is considered the ultimate goal in scientific discovery. Artificial intelligence offers a promising avenue to greatly accelerate this process. Existing methods often depend on large datasets, strong assumptions about objective functions, and classic machine learning techniques, restricting their effectiveness to low-dimensional or data-rich problems. Here we introduce an optimization pipeline that can effectively tackle complex, high-dimensional problems with limited data. This approach utilizes a deep neural surrogate to iteratively find optimal solutions and introduces additional mechanisms to avoid local optima, thereby minimizing the required samples. Our method finds superior solutions in problems with up to 2,000 dimensions, whereas existing approaches are confined to 100 dimensions and need considerably more data. It excels across varied real-world systems, outperforming current algorithms and enabling efficient knowledge discovery. Although focused on scientific problems, its benefits extend to numerous quantitative fields, paving the way for advanced self-driving laboratories.

报告人简介/Curriculum Vitae:

韦业香港城市大学数据科学系和材料系助理教授/博士生导师。于2014和2018年分别在荷兰特文特大学和德国亚琛工业大学获得物理学本科和硕士学位，并于2021年在德国马普学会可持续材料与智能系统研究所完成了博士学位。博士毕业后分别在清华大学交叉信息研究院(2021-2023)和瑞士洛桑联邦理工学院(2023-2024)开展基于人工智能的计算方法以及相关应用。他的研究兴趣包括数据驱动优化方法、大语言模型以及物理启发的机器学习，以及使用这些方法解决真实复杂系统中的高维非线性问题, 相关工作发表在Science, Nature Computational Science, Nature Communications, Advanced Science 等期刊 (一作或通讯), 并被MIT technology Review, Chemistry World 等著名科技媒体报道。