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Machine learning-aided multiscale design of porous materials tailored to application-specific, hydro-mechanical performance requirements

CORDIS · observation · Publication date unknown

Machine learning-aided multiscale design of porous materials tailored to application-specific, hydro-mechanical performance requirements Through continuous interaction between computational fluid dynamics, mechanics of solids, material engineering, and machine learning, with my host, I will develop a novel and computationally efficient method, implemented in open-source software, for the multi-scale design of engineered porous materials (EPMs) that meet user-specified hydro-mechanical functional requirements. This computer-aided approach will accelerate the discovery of EPMs and shorten the time for technology development, and is aimed at EPM design for additive Manufacturing (i.e. 3D-printing). The basic notion of the proposed approach is: (1) to employ a dimensionality reduction techniques to obtain a low-dimensional proxy for the high-dimensional problem of characterizing a porous mic

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recordType

award

status

SIGNED

region

EU

value

191760

unit

EUR