Accelerated multiscale mechanics modeling in a deep learning framework
Authors:
- Ashwini GuptaAGAshwini Gupta
- Anindya BhaduriABAnindya Bhaduri
- Lori Graham-BradyLGLori Graham-Brady
Top Cited Papers
- 7 June 2023
- journal article
- research article
- Published by Elsevier in Mechanics of Materials
- Vol. 184
Abstract
No abstract availableKeywords
All Related Versions
Funding Information
- Army Research Laboratory (W911NF-12-2-0022, W911NF-12-2-0023, W911NF-22-2-0014)
This publication has 35 references indexed in Scilit:
- Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materialsComputer Methods in Applied Mechanics and Engineering, 2016
- How to represent crystal structures for machine learning: Towards fast prediction of electronic propertiesPhysical Review B, 2014
- Multiscale Modeling of Composite Materials: a Roadmap Towards Virtual TestingAdvanced Materials, 2011
- Multi-scale computational homogenization: Trends and challengesJournal of Computational and Applied Mathematics, 2010
- Microstructure sensitive design for performance optimizationProgress in Materials Science, 2010
- Material spatial randomness: From statistical to representative volume elementProbabilistic Engineering Mechanics, 2006
- Multi‐scale modeling of heterogeneous structures with inelastic constitutive behaviourEngineering Computations, 2005
- FE2 multiscale approach for modelling the elastoviscoplastic behaviour of long fibre SiC/Ti composite materialsComputer Methods in Applied Mechanics and Engineering, 2000
- Computational Design of Hierarchically Structured MaterialsScience, 1997
- Application of variational concepts to size effects in elastic heterogeneous bodiesJournal of the Mechanics and Physics of Solids, 1990