Výsledky vyhledávání - "Kapteyn, Michael"

Report

Predictive Digital Twin for Optimizing Patient-Specific Radiotherapy Regimens under Uncertainty in High-Grade Gliomas

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

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Report

A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale

Autor: Kapteyn, Michael G., Pretorius, Jacob V. R., Willcox, Karen E.

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

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Report

From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning

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

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Akademický článek

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Dissertation/ Thesis

Mathematical and Computational Foundations to Enable Predictive Digital Twins at Scale

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

Externí odkaz: https://hdl.handle.net/1721.1/139514
https://orcid.org/0000-0002-3594-682X

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Dissertation/ Thesis

Distributionally robust optimization for design under partially observable uncertainty

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

Externí odkaz: http://hdl.handle.net/1721.1/119304

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