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Optimal control of aging in complex networks.
Sun, Eric D; Michaels, Thomas C T; Mahadevan, L.
Afiliación
  • Sun ED; School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.
  • Michaels TCT; School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.
  • Mahadevan L; School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138; lmahadev@g.harvard.edu.
Proc Natl Acad Sci U S A ; 117(34): 20404-20410, 2020 08 25.
Article en En | MEDLINE | ID: mdl-32817469
Many complex systems experience damage accumulation, which leads to aging, manifest as an increasing probability of system collapse with time. This naturally raises the question of how to maximize health and longevity in an aging system at minimal cost of maintenance and intervention. Here, we pose this question in the context of a simple interdependent network model of aging in complex systems and show that it exhibits cascading failures. We then use both optimal control theory and reinforcement learning alongside a combination of analysis and simulation to determine optimal maintenance protocols. These protocols may motivate the rational design of strategies for promoting longevity in aging complex systems with potential applications in therapeutic schedules and engineered system maintenance.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2020 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Guideline Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2020 Tipo del documento: Article Pais de publicación: Estados Unidos