Titlebook: Machine Learning-Augmented Spectroscopies for Intelligent Materials Design; Nina Andrejevic Book 2022 The Editor(s) (if applicable) and Th

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修剪過的樹籬

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Nina AndrejevicNominated as an outstanding PhD thesis by Massachusetts Institute of Technology.Introduces machine learning methods for neutron and photon scattering and spectroscopy.Identifies spectral signatures of

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Conclusion and Outlook,In this chapter, we summarize the primary contributions of this thesis work and offer a short perspective on the possible extensions of each study, concluding with a discussion of outstanding challenges and emerging approaches in the field.

職業(yè)

https://doi.org/10.1007/978-3-031-14808-8machine learning for materials characterization; machine learning Raman spectra; machine learning neut

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Brocas-Area

Nina Andrejevicrked control and multi-agent systems.Written by experts in tThis authored monograph presents a study on fundamental limits and robustness of stability and stabilization of time-delay systems, with an emphasis on time-varying delay, robust stabilization, and newly emerged areas such as networked cont

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Introduction,ctural and dynamical properties at atomic to mesoscopic length scales. As advances at scientific user facilities enable the collection of ever larger data volumes in higher-dimensional parameter spaces, the design, analysis, and interpretation of such experiments becomes both increasingly valuable a