Empowering Materials Processing and Performance from Data and AI
Article dans une revue avec comité de lecture
Auteur
CHINESTA SORIA, Francisco CUETO, EliasKLUSEMANN, BenjaminRésumé
Third millennium engineering is addressing new challenges in materials sciences and engineering [...]
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Article dans une revue avec comité de lecture; ;CUETO, Elias; CHINESTA SORIA, Francisco (Frontiers Media SA, 2022-05)
We propose an efficient method to determine the micro-structural entropic behavior of polymer chains directly from a sufficiently rich non-homogeneous experiment at the continuum scale. The procedure is developed in 2 ...
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Article dans une revue avec comité de lecture;CHINESTA SORIA, Francisco; CUETO, Elias (Elsevier, 2021)
We present a method for the data-driven learning of physical phenomena whose evolution in time depends on history terms. It is well known that a Mori-Zwanzig-type projection produces a description of the physical phenomena ...
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Article dans une revue avec comité de lecture; ; ; ; ; ;CUETO, Elias; ; CHINESTA SORIA, Francisco (EDP Sciences, 2021)
Solving mechanical problems in large structures with rich localized behaviors remains a challenging issue despite the enormous advances in numerical procedures and computational performance. In particular, these localized ...
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Article dans une revue avec comité de lectureGHNATIOS, Chady; ; ; ; CUETO, Elias; AMMAR, Amine; CHINESTA SORIA, Francisco (Wiley, 2020)
Composite manufacturing processes usually proceed from preimpregnated preforms that are consolidated by simultaneously applying heat and pressure, so as to ensure a perfect contact compulsory for making molecular diffusion ...
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Article dans une revue avec comité de lecture; ; ;CHINESTA SORIA, Francisco; CUETO, Elias (Elsevier, 2021)
We develop a method to learn physical systems from data that employs feedforward neural networks and whose predictions comply with the first and second principles of thermodynamics. The method employs a minimum amount of ...