相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Machine learning in nuclear materials research
Dane Morgan et al.
CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE (2022)
Recurrent neural network modeling of the large deformation of lithium-ion battery cells
Thomas Tancogne-Dejean et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2021)
Hybrid FEM-NN models: Combining artificial neural networks with the finite element method
Sebastian K. Mitusch et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2021)
Learning constitutive relations using symmetric positive definite neural networks
Kailai Xu et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2021)
Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
Nikolaos N. Vlassis et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)
Deep learning method for determining the surface elastic moduli of microstructured solids
Sang Ye et al.
EXTREME MECHANICS LETTERS (2021)
Physics-driven machine learning model on temperature and time-dependent deformation in lithium metal and its finite element implementation
Jici Wen et al.
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2021)
Knowledge extraction and transfer in data-driven fracture mechanics
Xing Liu et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2021)
SciPy 1.0: fundamental algorithms for scientific computing in Python
Pauli Virtanen et al.
NATURE METHODS (2020)
Using neural networks to represent von Mises plasticity with isotropic hardening
Annan Zhang et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2020)
Exploring Elastoplastic Constitutive Law of Microstructured Materials Through Artificial Neural Network-A Mechanistic-Based Data-Driven Approach
Hang Yang et al.
JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME (2020)
Extraction of mechanical properties of materials through deep learning from instrumented indentation
Lu Lu et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)
On the potential of recurrent neural networks for modeling path dependent plasticity
Maysam B. Gorji et al.
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2020)
A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
Ling Wu et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)
PPINN: Parareal physics-informed neural network for time-dependent PDEs
Xuhui Meng et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)
Learning constitutive relations from indirect observations using deep neural networks
Daniel Z. Huang et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2020)
Strain rate and temperature dependent fracture of aluminum alloy 7075: Experiments and neural network modeling
Kedar S. Pandya et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2020)
Machine-learning based temperature- and rate-dependent plasticity model: Application to analysis of fracture experiments on DP steel
Xueyang Li et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2019)
Application of artificial neural networks in micromechanics for polycrystalline metals
Usman Ali et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2019)
High-entropy alloys
Easo P. George et al.
NATURE REVIEWS MATERIALS (2019)
Deep neural network method for predicting the mechanical properties of composites
Sang Ye et al.
APPLIED PHYSICS LETTERS (2019)
Tuning element distribution, structure and properties by composition in high-entropy alloys
Qingqing Ding et al.
NATURE (2019)
Unified Form Language: A Domain-Specific Language for Weak Formulations of Partial Differential Equations
Martin S. Alnaes et al.
ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE (2014)
Prediction of nonlinear viscoelastic behavior of polymeric composites using an artificial neural network
MS Al-Haik et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2006)