Prediction of nonlocal elasticity parameters using high-throughput molecular dynamics simulations and machine learning
DOI10.1016/J.EUROMECHSOL.2023.105175MaRDI QIDQ6181394
Abhiram B. R., Debraj Ghosh, Hridya P. Lal
Publication date: 2 January 2024
Published in: European Journal of Mechanics. A. Solids (Search for Journal in Brave)
neural networkboundary value problemsupport vector machineGaussian process regressionsupervised machine learningstrain gradient theorylength-scale parameter
Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Elastic materials (74B99) Numerical and other methods in solid mechanics (74S99) Molecular, statistical, and kinetic theories in solid mechanics (74A25)
Cites Work
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- Analysis of micro-sized beams for various boundary conditions based on the strain gradient elasticity theory
- Static and dynamic analysis of micro beams based on strain gradient elasticity theory
- Continuum interpretation of virial stress in molecular simulations
- A novel atomistic approach to determine strain-gradient elasticity constants: tabulation and comparison for various metals, semiconductors, silica, polymers and the (ir)relevance for nanotechnologies
- Elastic materials with couple-stresses
- Fast parallel algorithms for short-range molecular dynamics
- Experiments and theory in strain gradient elasticity.
- Strain gradient elasticity and modified couple stress models for buckling analysis of axially loaded micro-scaled beams
- Couple stress based strain gradient theory for elasticity
- Thermodynamics of fractional-order nonlocal continua and its application to the thermoelastic response of beams
- Effects of couple-stresses in linear elasticity
- Data-Driven Science and Engineering
- The Art of Molecular Dynamics Simulation
- A neural network-based approach for bending analysis of strain gradient nanoplates
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