Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

2023/07/17 by Hanxun Jin, Enrui Zhang, Horacio D. Espinosa · 143 citations
Engineering · Materials Science · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Computer science #Data science #Engineering #Field (mathematics) #Hydrogen embrittlement and corrosion behaviors in metals #Machine Learning in Materials Science #Management science #Mathematics #Micromechanics #Model Reduction and Neural Networks #Physics #Solid mechanics #Variety (cybernetics)

paper · doi:10.1115/1.4062966

published in Applied Mechanics Reviews 75(6) (American Society of Mechanical Engineers)

openalex publication_date 2023/07/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Abstract For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel artificial materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is growing exponentially, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano- and micromechanics, architected materials, and two-dimensional materials. Finally, we highlight some current challenges of applying ML to multimodality and multifidelity experimental datasets, quantifying the uncertainty of ML predictions, and proposing several future research directions. This review aims to provide valuable insights into the use of ML methods and a variety of examples for researchers in solid mechanics to integrate into their experiments.

Citations

• ImageNet classification with deep convolutional neural networks
• Generative adversarial networks

Cited by

• Harnessing disorder to decouple extension and shear in kirigami metamaterials
• pyALDIC: A Python Implementation of Augmented Lagrangian Digital Image Correlation with a GUI, Adaptive Meshing, and Mask-Aware Subset Splitting
• Design-GenNO: A Physics-Informed Generative Model with Neural Operators for Inverse Microstructure Design
• A DEM-driven machine learning framework for abrasive wear prediction
• A neural network machine-learning approach for characterising hydrogen trapping parameters from TDS experiments
• Enhancing material behavior discovery using embedding-oriented Physically-Guided Neural Networks with Internal Variables
• A physics-informed neural network for modeling fracture without gradient damage: formulation, application, and assessment
• Integrated Finite Element Neural Network (IFENN) for Phase-Field Fracture with Minimal Input and Generalized Geometry-Load Handling
• Preface to the theme issue 'physics-informed machine learning and its structural integrity applications'. [europepmc]
• Rational Design of Flexible Mechanical Force Sensors for Healthcare and Diagnosis. [europepmc]
• Detection of cardiovascular disease cases using advanced tree-based machine learning algorithms. [europepmc]
• Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields. [europepmc]
• Probing the Nanoscale Onset of Plasticity in Electroplated Copper for Hybrid Bonding Structures via Multimodal Atomic Force Microscopy. [europepmc]
• A Review of Finite Element Analysis in Spine Surgery Decision-Making. [europepmc]
• Mechanics-informed machine learning for predicting mechanical properties of heat-treated multi-metal alloys. [europepmc]

Related

• A review of the application of machine learning and data mining approaches in continuum materials mechanics
• Physics-based Machine Learning for Computational Fracture Mechanics
• Opportunities and Challenges for Machine Learning in Materials Science
• Machine Learning for Material Characterization with an Application for Predicting Mechanical Properties
• Reduced and All-at-Once Approaches for Model Calibration and Discovery in Computational Solid Mechanics