中国科学技术大学本硕博，曾任美国北卡罗莱纳大学夏洛特分校博士后、德国斯图加特大学洪堡学者。2010年任上海交通大学特别研究员，2016年晋升正教授，2019-2021年任数学学院副院长，2021年起任教务处副处长。担任AAMM、CMS和MCA等杂志编委。曾获上海市和国家级教学成果奖、上海交通大学十大科技进展、上海市自然科学奖等。研究方向为快速算法和高性能计算、分子动力学算法和偏微分方程的数值方法等，发表80多篇研究论文。
The development of efficient methods for long-range systems plays important role in all-atom simulations of biomolecules and materials science. This talk reviews recent progress of random batch molecular dynamics, including random-batch Ewald and random-batch sum-of-Gaussians (SOG) method, together with the package development. These algorithms take advantage of the random minibatch strategy for the force calculation between particles, leading to an order N algorithm. It is based on the Ewald or the SOG splitting of the Coulomb kernel and the random importance sampling is employed for the Fourier part, thus avoiding the use of the FFT and greatly improving the scalability of the molecular simulations, achieving 1 order of magnitude faster than classical lattice-based methods. Numerical and application examples are presented to show the attractive performance of our methods.
