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Learning from learning algorithms: application to attosecond dynamics of high-harmonic generation

dc.contributor.authorRabitz, Herschel, author
dc.contributor.authorChristov, Ivan, author
dc.contributor.authorKapteyn, Henry C., author
dc.contributor.authorMurnane, Margaret M., author
dc.contributor.authorBartels, Randy A., author
dc.contributor.authorAmerican Physical Society, publisher
dc.date.accessioned2007-01-03T08:11:15Z
dc.date.available2007-01-03T08:11:15Z
dc.date.issued2004
dc.description.abstractUsing experiment and modeling, we show that the data set generated when a learning algorithm is used to optimize a quantum system can help to uncover the physics behind the process being optimized. In particular, by optimizing the process of high-harmonic generation using shaped light pulses, we generate a large data set and analyze its statistical behavior. This behavior is then compared with theoretical predictions, verifying our understanding of the attosecond dynamics of high-harmonic generation and uncovering an anomalous region of parameter space.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationBartels, Randy A., et al., Learning from Learning Algorithms: Application to Attosecond Dynamics of High-Harmonic Generation, Physical Review. A 70, no. 4 (October 2004): 043404-1-043404-5.
dc.identifier.urihttp://hdl.handle.net/10217/68109
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©2004 American Physical Society.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.titleLearning from learning algorithms: application to attosecond dynamics of high-harmonic generation
dc.typeText

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