Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Kapteyn, Michael"'
Autor:
Chaudhuri, Anirban, Pash, Graham, Hormuth II, David A., Lorenzo, Guillermo, Kapteyn, Michael, Wu, Chengyue, Lima, Ernesto A. B. F., Yankeelov, Thomas E., Willcox, Karen
Publikováno v:
Frontiers in Artificial Intelligence, 6, 2023
We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the und
Externí odkaz:
http://arxiv.org/abs/2308.12429
Autor:
Kapteyn, Michael G.
A digital twin is a computational model that evolves over time to persistently represent a unique physical asset. Digital twins underpin intelligent automation by enabling asset-specific analysis and data-driven decision-making. Although the promise
A unifying mathematical formulation is needed to move from one-off digital twins built through custom implementations to robust digital twin implementations at scale. This work proposes a probabilistic graphical model as a formal mathematical represe
Externí odkaz:
http://arxiv.org/abs/2012.05841
Autor:
Kapteyn, Michael G., Willcox, Karen E.
This work develops a methodology for creating a data-driven digital twin from a library of physics-based models representing various asset states. The digital twin is updated using interpretable machine learning. Specifically, we use optimal trees---
Externí odkaz:
http://arxiv.org/abs/2004.11356
Autor:
Kapteyn, Michael George
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 81-83).
Deciding how to represent and manage uncert
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 81-83).
Deciding how to represent and manage uncert
Externí odkaz:
http://hdl.handle.net/1721.1/119304
Akademický článek
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Akademický článek
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Publikováno v:
Prof. Willcox via Barbara Williams
Deciding how to represent and manage uncertainty is a vital part of designing complex systems. Widely used is a probabilistic approach—assigning a probability distribution to each uncertain variable. However, this presents the designer with the tas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90dbcf064f359b8c8de9ca34fd83e0ca
https://doi.org/10.2514/6.2018-0666
https://doi.org/10.2514/6.2018-0666
Autor:
Kapteyn MG; Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA., Pretorius JVR; The Jessara Group, Austin, TX, USA., Willcox KE; Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA. kwillcox@oden.utexas.edu.
Publikováno v:
Nature computational science [Nat Comput Sci] 2021 May; Vol. 1 (5), pp. 337-347. Date of Electronic Publication: 2021 May 20.