An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials
出版年份 2023 全文链接
标题
An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials
作者
关键词
-
出版物
npj Computational Materials
Volume 9, Issue 1, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2023-03-13
DOI
10.1038/s41524-023-00991-z
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Lossless multi-scale constitutive elastic relations with artificial intelligence
- (2022) Jaber Rezaei Mianroodi et al. npj Computational Materials
- Learning the Stress-Strain Fields in Digital Composites using Fourier Neural Operator
- (2022) Meer Mehran Rashid et al. iScience
- Dislocation nucleation in Al single crystal at shear parallel to (111) plane: Molecular dynamics simulations and nucleation theory with artificial neural networks
- (2021) Alexander E. Mayer et al. INTERNATIONAL JOURNAL OF PLASTICITY
- Deep learning model to predict complex stress and strain fields in hierarchical composites
- (2021) Zhenze Yang et al. Science Advances
- Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials
- (2021) Jaber Rezaei Mianroodi et al. npj Computational Materials
- Spectral methods for full-field micromechanical modelling of polycrystalline materials
- (2020) Ricardo A. Lebensohn et al. COMPUTATIONAL MATERIALS SCIENCE
- Strain rate and temperature dependent fracture of aluminum alloy 7075: Experiments and neural network modeling
- (2020) Kedar S. Pandya et al. INTERNATIONAL JOURNAL OF PLASTICITY
- Application of artificial neural networks in micromechanics for polycrystalline metals
- (2019) Usman Ali et al. INTERNATIONAL JOURNAL OF PLASTICITY
- A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics
- (2019) Frederic E. Bock et al. Frontiers in Materials
- DBFFT: A displacement based FFT approach for non-linear homogenization of the mechanical behavior
- (2019) S. Lucarini et al. INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE
- A hybrid approach to simulate the homogenized irreversible elastic–plastic deformations and damage of foams by neural networks
- (2019) Christoph Settgast et al. INTERNATIONAL JOURNAL OF PLASTICITY
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- (2018) M. Raissi et al. JOURNAL OF COMPUTATIONAL PHYSICS
- DAMASK – The Düsseldorf Advanced Material Simulation Kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale
- (2018) F. Roters et al. COMPUTATIONAL MATERIALS SCIENCE
- Identifying Structure–Property Relationships Through DREAM.3D Representative Volume Elements and DAMASK Crystal Plasticity Simulations: An Integrated Computational Materials Engineering Approach
- (2017) Martin Diehl et al. JOM
- FFT-based homogenization for microstructures discretized by linear hexahedral elements
- (2016) Matti Schneider et al. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
- Fourier-based schemes for computing the mechanical response of composites with accurate local fields
- (2015) François Willot COMPTES RENDUS MECANIQUE
- Numerically robust spectral methods for crystal plasticity simulations of heterogeneous materials
- (2015) P. Shanthraj et al. INTERNATIONAL JOURNAL OF PLASTICITY
- Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications
- (2009) F. Roters et al. ACTA MATERIALIA
- Nonlinear constitutive models from nanoindentation tests using artificial neural networks
- (2007) R HAJALI et al. INTERNATIONAL JOURNAL OF PLASTICITY
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