Learning dynamical models across physical systems
INVITED · T14 · ID: 24424
Presentations
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Learning dominant physical processes with data-driven balance models
ORAL · Invited
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Presenters
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Bing Brunton
University of Washington
Authors
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Bing Brunton
University of Washington
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Using Knowledge-based Neural Ordinary Differential Equations to Learn Complex Dynamics and Chaos
ORAL · Invited
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Presenters
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M. Ani Hsieh
University of Pennsylvania
Authors
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M. Ani Hsieh
University of Pennsylvania
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DBIO Dissertation Award (2021): Physics of Behavior Across Scales
ORAL · Invited
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Publication: 1) Helms, Rozemuller, Costa, Avery, Stephens and Shimizu; Modelling the ballistic-to-diffusive transition in nematode motility reveals variation in exploration across species. Journal of the Royal Society Interface 16, 20190174 (2019);<br>2) Costa, Ahamed, Stephens; Adaptive, locally-linear models of complex dynamics, Proceedings of the National Academy of Sciences of the United States of America 116,1501 (2019);<br>3) Ahamed, Costa, Stephens; Capturing the Continuous Complexity of Behavior in C. elegans, Nature Physics 17, 275–283 (2021)<br>4) Costa, Ahamed, Jordan, Stephens; Maximally-predictive ensemble dynamics from data arXiv:2105.12811 (2021)
Presenters
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Antonio Carlos Costa
Ecole Normale Superieure Paris
Authors
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Antonio Carlos Costa
Ecole Normale Superieure Paris
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Machine learning approaches to biomechanics
ORAL · Invited
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Presenters
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Vincenzo Vitelli
University of Chicago
Authors
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Vincenzo Vitelli
University of Chicago
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Physics-informed machine learning: climate modeling and COVID-19 forecasting
ORAL · Invited
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Presenters
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Rose Yu
University of California San Diego
Authors
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Rose Yu
University of California San Diego
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