孙浩，1988年生，中国人民大学高瓴人工智能学院“长聘副教授、博导”，国家高层次人才青年专家，麻省理工学院兼职研究员、美国东北大学兼职教授。2014年在美国哥伦比亚大学取得工程力学博士学位，随后在麻省理工学院从事博士后研究，曾任美国匹兹堡大学、美国东北大学终身序列助理教授、博导。主要从事科学智能、人工智能数理基础与交叉前沿研究，包含可诠释性深度学习、物理启发深度学习、符号强化学习与推理、数据驱动复杂动力系统建模与识别、基础设施健康监测与智能化管理等方向。在国际一流SCI期刊（如《自然-通讯》）和计算机顶会（如NeurIPS、ICLR）等各类重要刊物上共发表论文50余篇；主持和共同主持国家自然科学基金委、美国科学基金委、华为科技公司等基础和应用研究项目十余项；研究成果受到了几十家国际知名媒体的广泛报导（例如《福克斯新闻》、《科学日报》、《麻省理工科技评论》等）。2018年入选福布斯北美“30位30岁以下精英榜（科学类）”，2019年当选“美国十大华人杰出青年”。

报告内容简介：

Harnessing data to model complex physical systems has become a critical scientific problem in many science and engineering areas. The state-of-the-art advances of AI (in particular deep learning, thanks to its rich representations for learning complex nonlinear functions) have great potential to tackle this challenge, but in general (i) rely on a large amount of rich data to train a robust model, (ii) have generalization and extrapolation issues, and (iii) lack of interpretability and explainability, with little physical meaning. To bridge the knowledge gaps between AI and complex physical systems in the sparse/small data regime, this talk will introduce the integration of bottom-up (data-driven) and top-down (physics-based) processes through a Physics-informed/encoded Deep Learning paradigm for modeling, simulation and discovery of complex physical systems. This talk will show examples on data-driven modeling of nonlinear PDEs that govern the behavior of complex physical systems, e.g., wave propagation, reaction-diffusion processes, fluid flows, etc.