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Journal Articles

A Data-Driven Model to Predict Constitutive and Failure Behavior of Elastomers Considering the Strain Rate, Temperature, and Filler Ratio

Aref Ghaderi, Roozbeh Dargazany

Publisher: ASME

J. Appl. Mech. May 2023, 90(5): 051010.

Paper No: JAM-22-1374

https://doi.org/10.1115/1.4056705

Published Online: February 8, 2023

View Article titled, A Data-Driven Model to Predict Constitutive and Failure Behavior of Elastomers Considering the Strain Rate, Temperature, and Filler Ratio

Topics: Artificial neural networks, Damage, Deformation, Elastomers, Engines, Fillers (Materials), Physics, Polymers, Rubber, Stress

Journal Articles

Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model

Markus J. Buehler

Publisher: ASME

J. Appl. Mech. December 2022, 89(12): 121009.

Paper No: JAM-22-1281

https://doi.org/10.1115/1.4055730

Published Online: October 6, 2022

View Article titled, Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model

Topics: Artificial neural networks, Diffusion (Physics), Dynamics (Mechanics), Fracture (Materials), Fracture (Process), Modeling, Molecular dynamics simulation, Potential energy, Simulation

Includes: Supplementary data

Journal Articles

Component-Based Machine Learning Paradigm for Discovering Rate-Dependent and Pressure-Sensitive Level-Set Plasticity Models

Nikolaos N. Vlassis, WaiChing Sun

Publisher: ASME

J. Appl. Mech. February 2022, 89(2): 021003.

Paper No: JAM-21-1305

https://doi.org/10.1115/1.4052684

Published Online: November 1, 2021

View Article titled, Component-Based Machine Learning Paradigm for Discovering Rate-Dependent and Pressure-Sensitive Level-Set Plasticity Models

Topics: Artificial neural networks, Hardening, Machine learning, Plasticity, Stress, Pressure

Journal Articles

Development of LSTM Networks for Predicting Viscoplasticity With Effects of Deformation, Strain Rate, and Temperature History

Lahouari Benabou

Publisher: ASME

J. Appl. Mech. July 2021, 88(7): 071008.

Paper No: JAM-21-1129

https://doi.org/10.1115/1.4051115

Published Online: May 26, 2021

View Article titled, Development of LSTM Networks for Predicting Viscoplasticity With Effects of Deformation, Strain Rate, and Temperature History

Topics: Deformation, Stress, Temperature, Viscoplasticity, Artificial neural networks, Solders, Plasticity, Alloys

Journal Articles

Exploring Elastoplastic Constitutive Law of Microstructured Materials Through Artificial Neural Network—A Mechanistic-Based Data-Driven Approach

Hang Yang, Hai Qiu, Qian Xiang, Shan Tang, Xu Guo

Publisher: ASME

J. Appl. Mech. September 2020, 87(9): 091005.

Paper No: JAM-20-1198

https://doi.org/10.1115/1.4047208

Published Online: June 4, 2020

View Article titled, Exploring Elastoplastic Constitutive Law of Microstructured Materials Through Artificial Neural Network—A Mechanistic-Based Data-Driven Approach

Topics: Artificial neural networks, Constitutive equations, Particulate matter, Plasticity, Stress, Cantilever beams

Journal Articles

Learning Nonlinear Constitutive Laws Using Neural Network Models Based on Indirectly Measurable Data

Xin Liu, Fei Tao, Haodong Du, Wenbin Yu, Kailai Xu

Publisher: ASME

J. Appl. Mech. August 2020, 87(8): 081003.

Paper No: JAM-20-1097

https://doi.org/10.1115/1.4047036

Published Online: May 14, 2020

View Article titled, Learning Nonlinear Constitutive Laws Using Neural Network Models Based on Indirectly Measurable Data

Topics: Artificial neural networks, Constitutive equations, Damage

Journal Articles

Constructing Multilayer Feedforward Neural Networks to Approximate Nonlinear Functions in Engineering Mechanics Applications

Jin-Song Pei, Eric C. Mai

Publisher: ASME

J. Appl. Mech. November 2008, 75(6): 061002.

https://doi.org/10.1115/1.2957600

Published Online: August 15, 2008

View Article titled, Constructing Multilayer Feedforward Neural Networks to Approximate Nonlinear Functions in Engineering Mechanics Applications

Topics: Artificial neural networks, Engineering prototypes, Feedforward control, Engineering mechanics

Journal Articles

Robust Adaptive Neural Estimation of Restoring Forces in Nonlinear Structures

E. B. Kosmatopoulos, A. W. Smyth, S. F. Masri, Mem. ASME, A. G. Chassiakos

Publisher: ASME

J. Appl. Mech. November 2001, 68(6): 880–893.

https://doi.org/10.1115/1.1408614

Published Online: June 8, 2001

View Article titled, Robust Adaptive Neural Estimation of Restoring Forces in Nonlinear Structures

Topics: Artificial neural networks, Errors, Excitation, Steel, Simulation

Journal Articles

Determination of Poisson’s Ratio by Spherical Indentation Using Neural Networks—Part I: Theory

N. Huber, A. Konstantinidis, Ch. Tsakmakis

Publisher: ASME

J. Appl. Mech. March 2001, 68(2): 218–223.

https://doi.org/10.1115/1.1354624

Published Online: November 1, 2000

View Article titled, Determination of Poisson’s Ratio by Spherical Indentation Using Neural Networks—Part I: Theory

Topics: Artificial neural networks, Deformation, Finite element analysis, Poisson ratio, Simulation, Stress, Hardening, Engineering simulation

Journal Articles

Determination of Poisson’s Ratio by Spherical Indentation Using Neural Networks—Part II: Identification Method

N. Huber, Ch. Tsakmakis

Publisher: ASME

J. Appl. Mech. March 2001, 68(2): 224–229.

https://doi.org/10.1115/1.1355032

Published Online: November 1, 2000

View Article titled, Determination of Poisson’s Ratio by Spherical Indentation Using Neural Networks—Part II: Identification Method

Topics: Artificial neural networks, Hardening, Poisson ratio, Finite element analysis

Journal Articles

Identification of Nonlinear Dynamic Systems Using Neural Networks

S. F. Masri, A. G. Chassiakos, T. K. Caughey

Publisher: ASME

J. Appl. Mech. March 1993, 60(1): 123–133.

https://doi.org/10.1115/1.2900734

Published Online: March 1, 1993

View Article titled, Identification of Nonlinear Dynamic Systems Using Neural Networks

Topics: Artificial neural networks, Nonlinear dynamical systems, Excitation, Engineering mechanics, Nonlinear systems

Journal citation

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Supplemental Material

A Data-Driven Model to Predict Constitutive and Failure Behavior of Elastomers Considering the Strain Rate, Temperature, and Filler Ratio

Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model

Component-Based Machine Learning Paradigm for Discovering Rate-Dependent and Pressure-Sensitive Level-Set Plasticity Models

Development of LSTM Networks for Predicting Viscoplasticity With Effects of Deformation, Strain Rate, and Temperature History

Exploring Elastoplastic Constitutive Law of Microstructured Materials Through Artificial Neural Network—A Mechanistic-Based Data-Driven Approach

Learning Nonlinear Constitutive Laws Using Neural Network Models Based on Indirectly Measurable Data

Constructing Multilayer Feedforward Neural Networks to Approximate Nonlinear Functions in Engineering Mechanics Applications

Robust Adaptive Neural Estimation of Restoring Forces in Nonlinear Structures

Determination of Poisson’s Ratio by Spherical Indentation Using Neural Networks—Part I: Theory

Determination of Poisson’s Ratio by Spherical Indentation Using Neural Networks—Part II: Identification Method

Identification of Nonlinear Dynamic Systems Using Neural Networks

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