清华主页 EN
导航菜单

Applied and Computational Math Colloquium | Multiscale Modeling of Arctic Sea Ice Floes

来源: 06-14

时间:Wed., 16:00-17:00, June 14, 2023

地点:Lecture hall, 3rd floor Jin Chun Yuan West Building

组织者:应用与计算数学团队

主讲人:Quanling Deng Australian National University

Abstract 

In this talk, I will start by presenting some quick facts about Arctic and Antarctic sea ice floes followed by a quick overview of the major sea ice continuum and particle models. I will then present our main contribution to its multiscale modelling.

The recent Lagrangian particle model based on the discrete element method (DEM) has shown improved model performance and started to gain more attention from the research groups that are working on Global Climate Models (GCMs). We adopt the DEM model for sea ice dynamical simulation. The major challenges are 1) model coupling in different frames of reference (Lagrangian for sea ice while Eulerian for the ocean and atmosphere dynamics); 2) the heavy computational cost when the number of the floes is large; and 3) inaccurate floe parameterisation when the floe distribution has multiscale features. To overcome these challenges, I will present a superfloe parameterisation to reduce the computational cost and a superparameterisation method to capture the multiscale features. In particular, the superfloe parameterisation facilitates noise inflation in data assimilation that recovers the unobserved ocean field underneath the sea ice. To capture the multiscale features, we adopt the Boltzmann equation for particles and superparameterise the sea ice floes as continuity equations governing the statistical moments. This leads to a particle-continuum coupled multiscale model. I will present several numerical experiments to demonstrate the success of the proposed method. This is joint work with Sam Stechmann (UW-Madison) and Nan Chen (UW-Madison).


About the speaker 

Quanling Deng

Australian National University

Dr. Quanling Deng is a Lecturer at the ANU School of Computing. He was born in Hunan, China and moved to the USA to study mathematics in August 2011. He graduated with a Ph.D. in computational mathematics with a topic on finite element analysis at the University of Wyoming in May 2016. He then joined Curtin University in Australia as a research associate and mainly contributed to the development of isogeometric analysis. He was a short-term visiting scholar at INRIA Paris, AGH University of Science and Technology in Poland, École des Ponts ParisTech (ENPC), USTC, and others. In March 2020, he joined the Department of Mathematics at the University of Wisconsin-Madison as a Van Vleck visiting assistant professor and worked on modelling and prediction of Arctic sea-ice dynamics. Dr. Deng has authored 35+ peer-reviewed publications. Also, he has given 15+ invited presentations and 20+ contributed talks at conferences and seminars and organised five mini-symposia at international conferences.

返回顶部
相关文章
  • Applied and Computational Math Colloquium

    Talk 1 9:00-10:00 Responsible Machine Learning and Machine Learning for Science 本报告分三部分:(一) 如何建立高效和可靠的机器学习系统;(二) 如何设计机器学习算法来解决科学问题,比如:药物开发和量子计算;(三) 对北卡罗来纳大学教堂山分校的简单分享。About the speaker 陈天龙博士将于 2024 年秋季加入北卡罗来纳大学教堂山分校计算机系担任助理教授。在这之前 (2023 - 2024),他会加入麻省理工和哈佛大学担任博士...

  • Adaptive Gradient Methods with Energy for Optimization Problems | Applied and Computational Math Colloquium

    AbstractWe propose AEGD, a new algorithm for gradient-based optimization of stochastic objective functions, based on adaptive updates of quadratic energy. The method is shown to be unconditionally energy stable, irrespective of the step size. In addition, AEGD enjoys tight convergence rates, yet allows a large step size. The method is straightforward to implement and requires little tuning of h...