Pandit, Prakul und Abdusalamov, Rasul und Itskov, Mikhail und Rege, Ameya Govind (2024) Deep reinforcement learning for microstructural optimisation of silica aerogels. Scientific Reports, 14, Seite 1511. Nature Publishing Group. doi: 10.1038/s41598-024-51341-y. ISSN 2045-2322.
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Offizielle URL: https://www.nature.com/articles/s41598-024-51341-y
Kurzfassung
Silica aerogels are being extensively studied for aerospace and transportation applications due to their diverse multifunctional properties. While their microstructural features dictate their thermal, mechanical, and acoustic properties, their accurate characterisation remains challenging due to their nanoporous morphology and the stochastic nature of gelation. In this work, a deep reinforcement learning (DRL) framework is presented to optimise silica aerogel microstructures modelled with the diffusion-limited cluster–cluster aggregation (DLCA) algorithm. For faster computations, two environments consisting of DLCA surrogate models are tested with the DRL framework for inverse microstructure design. The DRL framework is shown to effectively optimise the microstructure morphology, wherein the error of the material properties achieved is dependent upon the complexity of the environment. However, in all cases, with adequate training of the DRL agent, material microstructures with desired properties can be achieved by the framework. Thus, the methodology provides a resource-efficient means to design aerogels, offering computational advantages over experimental iterations or direct numerical solutions.
elib-URL des Eintrags: | https://elib.dlr.de/202191/ | ||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||
Titel: | Deep reinforcement learning for microstructural optimisation of silica aerogels | ||||||||||||||||||||
Autoren: |
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Datum: | 17 Januar 2024 | ||||||||||||||||||||
Erschienen in: | Scientific Reports | ||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||
Gold Open Access: | Ja | ||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||
Band: | 14 | ||||||||||||||||||||
DOI: | 10.1038/s41598-024-51341-y | ||||||||||||||||||||
Seitenbereich: | Seite 1511 | ||||||||||||||||||||
Verlag: | Nature Publishing Group | ||||||||||||||||||||
ISSN: | 2045-2322 | ||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||
Stichwörter: | dlca, drl, machine learning, surrogate, model, silica, aerogel, optimisation | ||||||||||||||||||||
HGF - Forschungsbereich: | keine Zuordnung | ||||||||||||||||||||
HGF - Programm: | keine Zuordnung | ||||||||||||||||||||
HGF - Programmthema: | keine Zuordnung | ||||||||||||||||||||
DLR - Schwerpunkt: | Digitalisierung | ||||||||||||||||||||
DLR - Forschungsgebiet: | D KIZ - Künstliche Intelligenz | ||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | D - ReBAR | ||||||||||||||||||||
Standort: | Köln-Porz | ||||||||||||||||||||
Institute & Einrichtungen: | Institut für Werkstoff-Forschung > Aerogele und Aerogelverbundwerkstoffe | ||||||||||||||||||||
Hinterlegt von: | Rege, Dr. Ameya Govind | ||||||||||||||||||||
Hinterlegt am: | 25 Jan 2024 09:37 | ||||||||||||||||||||
Letzte Änderung: | 25 Jan 2024 09:37 |
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