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Gifts from the superconducting curiosity shop

Frontiers of physics 2011年第4期6卷 347-349页

作者： David Mandrus Department of materials Science and Engineering University of Tennessee Knoxville TN 37996 USA materials Science and Engineering Division Oak Ridge National Laboratory Oak Ridge TN 37831 USA

Superconductivity has just celebrated its 100th birthday, and yet despite its advanced age it has never been more alive. Given that most subfields of materials physics have a half-life of about seven years, what accounts for the enduring popularity of superconductivity？ What is it about superconductivity that continues to fascinate？

关键词： superconducting materials physics accounts

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A perspective on regression and Bayesian approaches for system identification of pattern formation dynamics

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Theoretical & Applied Mechanics Letters 2020年第3期10卷 188-194页

作者： Zhenlin Wang Bowei Wu Krishna Garikipati Xun Huan Department of Mechanical Engineering University of MichiganAnn ArborMI 48109USA Oden Institute for Computational Engineering and Sciences University of Texas at AustinAustinTX 78712USA Departments of Mechanical Engineering and Mathematics Michigan Institute for Computational Discovery&EngineeringUniversity of MichiganAnn ArborMI 48109USA

We present two approaches to system identification, i.e. the identification of partial differentialequations (PDEs) from measurement data. The first is a regression-based variational systemidentification procedure that is advantageous in not requiring repeated forward model solves andhas good scalability to large number of differential operators. However it has strict data typerequirements needing the ability to directly represent the operators through the available *** second is a Bayesian inference framework highly valuable for providing uncertaintyquantification, and flexible for accommodating sparse and noisy data that may also be indirectquantities of interest. However, it also requires repeated forward solutions of the PDE modelswhich is expensive and hinders scalability. We provide illustrations of results on a model problemfor pattern formation dynamics, and discuss merits of the presented methods.

关键词： Computational mechanics materials physics Pattern formation Bayesian inference Inverse problem

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