SOURCE-LINKED INTELLIGENCE

Physics-embedded deep learning framework for next-generation microstructure design to enhance fibre bridging in composites

CORDIS · observation · Publication date unknown

Physics-embedded deep learning framework for next-generation microstructure design to enhance fibre bridging in composites Micromechanical finite element (FE) techniques have shown great promise in naturally predicting the key mechanism of fibre bridging, which can significantly improve toughness in fibre-reinforced composites. However, they remain too computationally expensive for practical microstructural design. I will build on the strengths of these micromechanical FE techniques to establish a new, scalable virtual design paradigm. To achieve this, I will develop a Physics-Embedded Deep Learning framework that embeds phase field theory within deep learning models. This innovative tool will enable rapid, high-fidelity virtual testing across a range of microstructures, thereby overcoming the computational barrier. The framework will be demonstrated by addressing the critical problem of transverse crack

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

260347.92

unit

EUR