StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials

[1]  Antonio Munjiza,et al.  A generalized anisotropic deformation formulation for geomaterials , 2016, 1805.06024.

[2]  Weihong Guo,et al.  Optimal Integration of Supervised Tensor Decomposition and Ensemble Learning for In Situ Quality Evaluation in Friction Stir Blind Riveting , 2021, IEEE Transactions on Automation Science and Engineering.

[3]  Tom Drummond,et al.  A review of deep learning in the study of materials degradation , 2018, npj Materials Degradation.

[4]  Hari S. Viswanathan,et al.  Predictive modeling of dynamic fracture growth in brittle materials with machine learning , 2018, Computational Materials Science.

[5]  Levent Burak Kara,et al.  Deep Learning for Stress Field Prediction Using Convolutional Neural Networks , 2018, ArXiv.

[6]  R. Haftka,et al.  Accelerating high-strain continuum-scale brittle fracture simulations with machine learning , 2021 .

[7]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[8]  Jianjun Shi,et al.  Tensor Mixed Effects Model With Application to Nanomanufacturing Inspection , 2018, Technometrics.

[9]  Bryan A. Moore,et al.  Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications , 2019, Computational Materials Science.

[10]  Ian A. Ashcroft,et al.  25 Numerical Approach: Finite Element Analysis , 2011 .

[11]  Wolfgang Ludwig,et al.  Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials , 2018, npj Computational Materials.

[12]  H. Milton Stewart,et al.  Surrogate Model Based Control Considering Uncertainties for Composite Fuselage Assembly , 2017 .

[13]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[14]  Philip S. Yu,et al.  PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs , 2017, NIPS.

[15]  Satish Karra,et al.  Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning , 2018, Scientific Reports.

[16]  Jianjun Shi,et al.  Virtual assembly and residual stress analysis for the composite fuselage assembly process , 2019, Journal of Manufacturing Systems.

[17]  Nao-Aki Noda,et al.  Strain rate concentration and dynamic stress concentration for double‐edge‐notched specimens subjected to high‐speed tensile loads , 2015 .

[18]  Hari S. Viswanathan,et al.  Learning to fail: Predicting fracture evolution in brittle materials using recurrent graph convolutional neural networks , 2018, Computational Materials Science.

[19]  C. Chatfield,et al.  Box‐Jenkins Seasonal Forecasting: Problems in a Case‐Study , 1973 .

[20]  Jieping Ye,et al.  Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction , 2018, AAAI.

[21]  Pascal Forquin,et al.  Brittle materials at high-loading rates: an open area of research , 2017, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[22]  Zihan Zhou,et al.  CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario , 2019, WWW.

[23]  Jeffrey H. Hunt,et al.  Feasibility analysis of composite fuselage shape control via finite element analysis , 2018 .

[24]  Jeffrey H. Hunt,et al.  Surrogate Model-Based Control Considering Uncertainties for Composite Fuselage Assembly , 2018 .

[25]  E. Gdoutos,et al.  Fracture Mechanics , 2020, Encyclopedic Dictionary of Archaeology.

[26]  Pietro Cornetti,et al.  Brittle Materials and Stress Concentrations: are they Able to withstand? , 2015 .

[27]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[28]  Tieniu Tan,et al.  Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning , 2018, ECCV.

[29]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[30]  Tao Zhou,et al.  Identification of a Suitable 3D Printing Material for Mimicking Brittle and Hard Rocks and Its Brittleness Enhancements , 2018, Rock Mechanics and Rock Engineering.

[31]  Guang-ren Shi.  Superiorities of support vector machine in fracture prediction and gassiness evaluation , 2008 .

[32]  Levent Burak Kara,et al.  Deep Learning for Stress Field Prediction Using Convolutional Neural Networks , 2018, J. Comput. Inf. Sci. Eng..

[33]  Shih-Fu Chang,et al.  CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Zhenhui Li,et al.  IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control , 2018, KDD.

[35]  Philip S. Yu,et al.  PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning , 2018, ICML.

[36]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[37]  A. J. Durelli,et al.  Brittle-material failures as indicators of stress-concentration factors , 1962 .

[38]  Dahua Lin,et al.  Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition , 2018, AAAI.

[39]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[40]  Y. Ma,et al.  Finite element analysis of residual stresses and thin plate distortion after face milling , 2015, 2015 12th International Bhurban Conference on Applied Sciences and Technology (IBCAST).

[41]  F. Leckie,et al.  Strength and Stiffness of Engineering Systems , 2009 .