Bridging physics and machine learning in material design and optimisation?
Abstrak
Lightweight materials have become integral in diverse sectors such as transportation, energy, and healthcare. Their varied microstructures and properties present significant potential for applications from load-bearing components to multifunctional structures. However, a major challenge lies in the heterogeneous material properties and vast design space of materials, impeding effective design and optimisation.My talk will address this challenge in two parts. Firstly, I will explore mechanics-based approaches to model the failure of materials. This will encompass a wide range of scenarios, from fracture, crushing behaviour, ballistic impact to liquid-solid impact of materials. Secondly, I will showcase the application of machine learning approaches for the design of porous architected materials, focusing on optimisation strategies. By bridging mechanics and machine learning, our work aims to unlock new possibilities in material design and optimisation.
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Sumber Database
- CrossRef
- DOI
- 10.52843/cassyni.s60964
- Akses
- Terbatas