報告人簡介
中國科學技術大學本碩博，曾任美國北卡羅萊納大學夏洛特分校博士后、德國斯圖加特大學洪堡學者。2010年任上海交通大學特別研究員，2016年晉升正教授，2019-2021年任數學學院副院長，2021年起任教務處副處長。擔任AAMM、CMS和MCA等雜志編委。曾獲上海市和國家級教學成果獎、上海交通大學十大科技進展、上海市自然科學獎等。研究方向為快速算法和高性能計算、分子動力學算法和偏微分方程的數值方法等，發表80多篇研究論文。
內容簡介
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.
