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dc.contributor.advisorJeremy England.en_US
dc.contributor.authorChvykov, Pavel.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Physics.en_US
dc.date.accessioned2020-01-08T19:31:30Z
dc.date.available2020-01-08T19:31:30Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/123349
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 2019en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 125-130).en_US
dc.description.abstractIn this work, I consider the possibility of using typicality-type arguments for understanding intractably complex damped-driven dynamical systems. By approximating such dynamics with appropriately constrained random process, I illustrate quantitative predictive power for some aspects of the motion. In particular, I argue that local dynamical stability, or exit rate, of a state is typically sufficient to predict steady-state probability in such systems -- circumventing the classic no-go theorems via our disorder approximation. I then focus on one consequence of this result: that the most likely long-time configurations should also be the dynamically stable ones. In a strongly-driven system, however, such stability may be hard to achieve, and therefore has interesting implications about the corresponding configurations: they must be well-adapted to the details of the driving forces, their dynamical robustness may be viewed in the context of self-healing, and depending on the drive, they can require substantial collective fine-tuning among the system's degrees of freedom. I confirm the emergence of such adapted states in several example systems, both in simulation and in experiment, and verify a quantitative agreement with the predicted scaling between their steady-state probability and local stability. I then explore several arguments and test-cases suggesting further generality of this framework. While it is not yet clear what the precise limits of applicability are for this approach, our results suggest that the intuition it builds can help with prediction and design in a broad class of complex dynamics.en_US
dc.description.statementofresponsibilityby Pavel Chvykov.en_US
dc.format.extent130 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectPhysics.en_US
dc.titleOn typicality and adaptation in driven dynamical systemsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Physicsen_US
dc.identifier.oclc1132797178en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Department of Physicsen_US
dspace.imported2020-01-08T19:31:29Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentPhysen_US


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