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【学术报告】研究生“灵犀学术殿堂”第472期之Piotr Breitkopf教授报告会通知
2019-05-26 15:03   党委学生工作部 理学院 审核人:   (点击: )

全校师生:

我校定于2019年5月30日举办研究生灵犀学术殿堂——Piotr Breitkopf教授报告会,现将有关事项通知如下:

1.报告会简介

报告人:Piotr Breitkopf教授

时间:2019年5月30日(星期四)上午10:30-12:10

地点:长安校区理学院数学系214会议室

报告题目:Model Order Reduction, Intrusive and Noninstrusive Approaches

模型降阶/缩减模型,侵入式和非侵入式近似方法

内容简介:

Industrial usage of numerical math-based tools such as the finite element method may in some applications become prohibitive due to the computational cost. This is particularly true in the automotive sector when optimizing the shape of a vehicle in crash situations. Model Order Reduction addresses this issue. Most model order reduction methods rely on the construction of a reduced basis to project the model on. The Proper Orthogonal Decomposition (POD) builds a modal basis from solution observations called snapshots. Data are in a first stage taken from full order model runs and then processed in a so-called off-line phase to give the reduced basis which is then used to build the reduced model. However, some difficulties arise in the POD. The data generated in the observation phase may become huge and hard to manipulate. Moreover, the computational cost for post-processing this data may as well explode. Another issue concerns the numerical integration schemes, i.e. the position of numerical integration points and the integration weights. Finally, the source code of the solver is not always available, requiring thus non-intrusive approaches.

在实际的工业应用中,力求高精度的数值计算(有限元法、有限体积法以及边界元法等)来获得精确的目标函数值和约束函数值,所需计算时间太长,而在优化过程中因需要多次这样的迭代,往往让计算量大到无法实现的地步。如何缩减理论的数值计算方法与实际复杂工程的巨大计算量的距离,成为不容忽视的一个难题,特别是在汽车行业,当优化车辆在碰撞情况下的形状时尤其如此,目前已有一些模型降阶方法来解决此类问题,但大多数模型降阶方法依赖于所构造的降阶基来对模型进行投影。利用正交分解(POD)从高精度的数值模型得到的快照(解的观测值)信息来构建一个模态基,然后在降维后的模型中进行近似求解。然而,这种思路仍存在一些困难,这体现在这种缩减模型的构造要求高精度的数值测量信息量必须很大,但对于一些复杂的实际问题,往往很难得到这样的大量信息;其次,对这些数据进行后处理的计算成本也可能会激增;此外,还会涉及到数值积分问题,即数值积分点的位置和积分权重的选取问题;最后,求解程序的源代码并不总是可用的,因此需要采用非侵入式算法。本报告重点对这种近似方式进行详细讲解。

2.欢迎各学院师生前来听报告。报告会期间请关闭手机或将手机调至静音模式。

党委学生工作部

理学院

2019年5月26日

报告人简介

Piotr Breitkopf is the head of the Multidisciplinary Design Optimization team at Université de Technologie de Compiègne (UTC), France. His research fields involve: computational mechanics, reduced order modeling, design optimization and high performance computing. He has obtained his PhD from Polish Academy of Sciences in 1988, and habilitation (HDR) from UTC in 1998. Since 2010 he is Deputy Director of Roberval Laboratory, a joint CNRS-UTC research unit. He is member of the steering committee of Labex MS2T. In 2014 he was nominated Oversea Expert of the Center for Foreign Talents Introduction and Academic Exchange of Mechanical Behavior of Advanced Structures and Materials at NPU. Together with Professor Zhang Weihong he presides the joint French-Chinese research group "Virtual Prototyping and Design". He serves at various editorial boards, scientific councils and scientific associations. He has authored and co-authored more than 200 peer reviewed journal papers, book chapters and referenced conference papers.

    Piotr Breitkopf法国科学院高级工程师,1988年获波兰科学院博士,1998年获法国贡比涅技术大学教授资格,研究领域涉及计算力学、缩减模型、优化设计、高性能计算等。是法国贡比涅技术大学多学科优化团队的带头人,2010年至今,担任法国科学院与贡比涅技术大学联合国家重点实验室(Roberval)副主任,Labex MS2T指导委员会成员。2014年被评为宝马娱乐app下载国外人才引进和先进结构材料力学行为学术交流中心的国外专家,他与张卫红教授共同主持中法联合研究小组“虚拟样机与设计”。是各种编辑委员会、科学委员会和科学协会任职。撰写并合著200多篇同行评议的SCI期刊论文、书籍章节和参考会议论文。

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