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RESEARCH PAPER · Computer Modeling in Engineering & Sciences · 2018

Machine Learning Models of Plastic Flow Based on Representation Theory

Reese E. Jones, Jeremy Alan Templeton, Clay M. Sanders, Jakob T. Ostien

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Machine Learning Models of Plastic Flow Based on Representation TheoryOriginalDeep Learning: New Engine for the Study of Material Microstructures and Physical Properties3Predicting the mechanical response of oligocrystals with deep learning4Predicting the mechanical response of oligocrystals with deep learning131TENSOR BASIS GAUSSIAN PROCESS MODELS OF HYPERELASTIC MATERIALS65Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model62SS-XGBoost: A Machine Learning Framework for Predicting Newmark Sliding Displacements of Slopes127Geometric deep learning for computational mechanics Part I: anisotropic hyperelasticity224Data-Driven multiscale modeling in mechanics195Machine Learning Neural-Network Predictions for Grain-Boundary Strain Accumulation in a Polycrystalline Metal22Data-Driven simulation of inelastic materials using structured data sets, tangent space information and transition rules.0Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates29Gaussian Process Regression constrained by Boundary Value Problems.1MESH-BASED GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR MODELING MATERIALS WITH MICROSTRUCTURE20Gaussian process regression constrained by boundary value problems26A NEURAL ORDINARY DIFFERENTIAL EQUATION FRAMEWORK FOR MODELING INELASTIC STRESS RESPONSE VIA INTERNAL STATE VARIABLES35On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling126Anisotropic yield models for lattice unit cell structures exploiting orthotropic symmetry4Model-free data-driven simulation of inelastic materials using structured data sets, tangent space information and transition rules39Quantifying Solid Solution Strengthening in Nickel-Based Superalloys via High-Throughput Experiment and Machine Learning4Perspective: Machine learning in experimental solid mechanics68Modular machine learning-based elastoplasticity: Generalization in the context of limited data72Deep learning and multi-level featurization of graph representations of microstructural data5DPK: Deep Neural Network Approximation of the First Piola-Kirchhoff Stress0Cooperative data-driven modeling15Neural Networks for Constitutive Modeling: From Universal Function Approximators to Advanced Models and the Integration of Physics100Data-driven physics-constrained recurrent neural networks for multiscale damage modeling of metallic alloys with process-induced porosity28Stress Representations for Tensor Basis Neural Networks: Alternative Formulations to Finger–Rivlin–Ericksen15Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics52Model-free chemomechanical interfaces: History-dependent damage under transient mass diffusion8Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response7A Review on Data-Driven Constitutive Laws for Solids142Mechanics-informed transformer-GCN for structural dynamic response prediction19GPM-PeNN: A generalized plasticity model-based data-driven constitutive modeling framework using physics-encoded neural network11A machine learning-based constitutive model incorporating history-dependent features for cyclic plasticity2A physics-augmented neural network framework for modeling and detecting thermo-visco-plastic behavior0

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  1. Deep Learning: New Engine for the Study of Material Microstructures and Physical Properties

    Modern Physics · 2019-01-01

    果 卢

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  2. Predicting the mechanical response of oligocrystals with deep learning

    arXiv (Cornell University) · 2019-01-30

    Ari Frankel, Reese E. Jones, Coleman N. Alleman, Jeremy Alan Templeton

    Read original paper ↗ ·

  3. Predicting the mechanical response of oligocrystals with deep learning

    Computational Materials Science · 2019-07-01

    Ari Frankel, Reese E. Jones, Coleman N. Alleman, Jeremy Alan Templeton

    Read original paper ↗ ·

  4. TENSOR BASIS GAUSSIAN PROCESS MODELS OF HYPERELASTIC MATERIALS

    Journal of Machine Learning for Modeling and Computing · 2020-01-01

    Ari Frankel, Reese E. Jones, Laura Painton Swiler

    Read original paper ↗ ·

  5. Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model

    Machine Learning Science and Technology · 2020-05-13

    Ari Frankel, Kousuke Tachida, Reese E. Jones

    Read original paper ↗ ·

  6. SS-XGBoost: A Machine Learning Framework for Predicting Newmark Sliding Displacements of Slopes

    Journal of Geotechnical and Geoenvironmental Engineering · 2020-06-18

    Mao‐Xin Wang, Duruo Huang, Gang Wang, D. Q. Li

    Read original paper ↗ ·

  7. Geometric deep learning for computational mechanics Part I: anisotropic hyperelasticity

    Computer Methods in Applied Mechanics and Engineering · 2020-08-25

    Nikolaos N. Vlassis, Ran Ma, WaiChing Sun

    Read original paper ↗ ·

  8. Data-Driven multiscale modeling in mechanics

    Journal of the Mechanics and Physics of Solids · 2020-11-20

    Konstantinos Karapiperis, Laurent Stainier, M. Ortíz, José E. Andrade

    Read original paper ↗ ·

  9. Machine Learning Neural-Network Predictions for Grain-Boundary Strain Accumulation in a Polycrystalline Metal

    Experimental Mechanics · 2021-01-20

    Renato Bichara Vieira, John Lambros

    Read original paper ↗ ·

  10. Data-Driven simulation of inelastic materials using structured data sets, tangent space information and transition rules.

    arXiv (Cornell University) · 2021-01-26

    Kerem Ciftci, Klaus Hackl

    Read original paper ↗ ·

  11. Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates

    npj Computational Materials · 2021-06-03

    Robert Saunders, Celia A. M. Butler, John G. Michopoulos, Dimitris C. Lagoudas, Alaa H. Elwany, Amit Bagchi

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  12. Gaussian Process Regression constrained by Boundary Value Problems.

    Unknown journal · 2021-07-01

    Mamikon Gulian

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  13. MESH-BASED GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR MODELING MATERIALS WITH MICROSTRUCTURE

    Journal of Machine Learning for Modeling and Computing · 2021-10-20

    Ari Frankel, Cosmin Safta, Coleman N. Alleman, Reese E. Jones

    Read original paper ↗ ·

  14. Gaussian process regression constrained by boundary value problems

    Computer Methods in Applied Mechanics and Engineering · 2021-10-25

    Mamikon Gulian, Ari Frankel, Laura Painton Swiler

    Read original paper ↗ ·

  15. A NEURAL ORDINARY DIFFERENTIAL EQUATION FRAMEWORK FOR MODELING INELASTIC STRESS RESPONSE VIA INTERNAL STATE VARIABLES

    Journal of Machine Learning for Modeling and Computing · 2022-01-01

    Reese E. Jones, Ari Frankel, Kyle L. Johnson

    Read original paper ↗ ·

  16. On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling

    Computer Methods in Applied Mechanics and Engineering · 2022-04-09

    Jan N. Fuhg, Nikolaos Bouklas

    Read original paper ↗ ·

  17. Anisotropic yield models for lattice unit cell structures exploiting orthotropic symmetry

    Computer Methods in Applied Mechanics and Engineering · 2022-04-23

    Jenmy Zimi Zhang, Adrian Butscher, S. Watts, Daniel A. Tortorelli

    Read original paper ↗ ·

  18. Model-free data-driven simulation of inelastic materials using structured data sets, tangent space information and transition rules

    Computational Mechanics · 2022-05-09

    Kerem Ciftci, Klaus Hackl

    Read original paper ↗ ·

  19. Quantifying Solid Solution Strengthening in Nickel-Based Superalloys via High-Throughput Experiment and Machine Learning

    Computer Modeling in Engineering & Sciences · 2022-10-27

    Zihang Li, Zexin Wang, Zi Wang, Zijun Qin, Feng Liu, Liming Tan, Xiaochao Jin, Xueling Fan, Lan Huang

    Read original paper ↗ ·

  20. Perspective: Machine learning in experimental solid mechanics

    Journal of the Mechanics and Physics of Solids · 2023-01-30

    Neal R. Brodnik, Caelin Muir, Nikhil Tulshibagwale, Jeff Rossin, McLean P. Echlin, Craig M. Hamel, Sharlotte Kramer, Tresa M. Pollock, James Doug Kiser, Christian W. Smith, Samantha Hayes Daly

    Read original paper ↗ ·

  21. Modular machine learning-based elastoplasticity: Generalization in the context of limited data

    Computer Methods in Applied Mechanics and Engineering · 2023-02-10

    Jan N. Fuhg, Craig M. Hamel, Kyle L. Johnson, Reese E. Jones, Nikolaos Bouklas

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  22. Deep learning and multi-level featurization of graph representations of microstructural data

    Computational Mechanics · 2023-03-31

    Reese E. Jones, Cosmin Safta, Ari Frankel

    Read original paper ↗ ·

  23. DPK: Deep Neural Network Approximation of the First Piola-Kirchhoff Stress

    Advances in Applied Mathematics and Mechanics · 2023-04-09

    Tianyi Hu, Jerry Zhijian Yang, Cheng Yuan

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  24. Cooperative data-driven modeling

    Computer Methods in Applied Mechanics and Engineering · 2023-09-16

    Aleksandr Dekhovich, O. Taylan Turan, Jiaxiang Yi, Miguel Aníbal Bessa

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  25. Neural Networks for Constitutive Modeling: From Universal Function Approximators to Advanced Models and the Integration of Physics

    Archives of Computational Methods in Engineering · 2023-10-17

    Johannes Dornheim, Lukas Morand, Hemanth Janarthanam Nallani, Dirk Helm

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  26. Data-driven physics-constrained recurrent neural networks for multiscale damage modeling of metallic alloys with process-induced porosity

    Computational Mechanics · 2024-01-11

    Shiguang Deng, Shirin Hosseinmardi, Libo Wang, Diran Apelian, Ramin Bostanabad

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  27. Stress Representations for Tensor Basis Neural Networks: Alternative Formulations to Finger–Rivlin–Ericksen

    Journal of Computing and Information Science in Engineering · 2024-02-01

    Jan N. Fuhg, Nikolaos Bouklas, Reese E. Jones

    Read original paper ↗ ·

  28. Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics

    Computer Methods in Applied Mechanics and Engineering · 2024-04-16

    Jan N. Fuhg, Reese E. Jones, Nikolaos Bouklas

    Read original paper ↗ ·

  29. Model-free chemomechanical interfaces: History-dependent damage under transient mass diffusion

    Computer Methods in Applied Mechanics and Engineering · 2024-08-22

    Lizhenhui Zhou, Wenyang Liu, Yiqi Mao, Shujuan Hou

    Read original paper ↗ ·

  30. Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response

    Computer Methods in Applied Mechanics and Engineering · 2024-08-27

    Ravi G. Patel, Cosmin Safta, Reese E. Jones

    Read original paper ↗ ·

  31. A Review on Data-Driven Constitutive Laws for Solids

    Archives of Computational Methods in Engineering · 2024-11-03

    Jan N. Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas, Bahador Bahmani, WaiChing Sun, Nikolaos N. Vlassis, Moritz Flaschel, Pietro Carrara, Laura De Lorenzis

    Read original paper ↗ ·

  32. Mechanics-informed transformer-GCN for structural dynamic response prediction

    Engineering Structures · 2024-12-13

    Qi Liao, Yuequan Bao, Haiyang Hu, Rongrong Hou

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  33. GPM-PeNN: A generalized plasticity model-based data-driven constitutive modeling framework using physics-encoded neural network

    Computer Methods in Applied Mechanics and Engineering · 2024-12-28

    Jingzhou Wang, Gang Ma, Tongming Qu, Shaoheng Guan, Wei Hong Zhou, Xiaolin Chang

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  34. A machine learning-based constitutive model incorporating history-dependent features for cyclic plasticity

    Computer Methods in Applied Mechanics and Engineering · 2025-10-31

    Ronak Shoghi, Alexander Hartmaier

    Read original paper ↗ ·

  35. A physics-augmented neural network framework for modeling and detecting thermo-visco-plastic behavior

    SSRN Electronic Journal · 2026-01-01

    Reese E. Jones, Asghar Arshad Jadoon, Daniel T. Seidl, Jan N. Fuhg

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