mlsanityData to 7 Oct 2026

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

arXiv 2303.07647 ↗First posted 14 Mar 2023cs.LG cond-mat.mtrl-scicited by 15

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Abstract (arXiv)

For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel 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 exploding, 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 micro-mechanics, architected materials, and 2D material. Finally, we highlight some current challenges of applying ML to multi-modality and multi-fidelity experimental datasets and propose several future research directions. This review aims to provide valuable insights into the use of ML methods as well as a variety of examples for researchers in solid mechanics to integrate into their experiments.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 55 of its 361 references that are in the map. First posted on arXiv 14 Mar 2023.

Builds on

55 of 361 references.

References · 55

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  2. 2021Physics-Informed Neural Operator for Learning Partial Differential Equationscited by 516
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50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 4; 2026: 4.

15 papers.

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  1. 2023Harnessing the Power of Neural Operators with Automatically Encoded Conservation Lawscited by 22
  2. 2024Large Language Model Agent as a Mechanical Designercited by 20
  3. 2023Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fieldscited by 7
  4. 2025Polyconvex Physics-Augmented Neural Network Constitutive Models in Principal Stretchescited by 5
  5. 2023Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networkscited by 3
  6. 2023Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operatorscited by 2
  7. 2024Learning Lagrangian Interaction Dynamics with Sampling-Based Model Order Reductioncited by 2
  8. 2024Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Spacecited by 0
  9. 2025CNN-powered micro- to macro-scale flow modeling in deformable porous mediacited by 0
  10. 2025Enhancing material behavior discovery using embedding-oriented Physically-Guided Neural Networks with Internal Variablescited by 0
  11. 2025A neural network machine-learning approach for characterising hydrogen trapping parameters from TDS experimentscited by 0
  12. 2026FilDeep: Learning Large Deformations of Elastic-Plastic Solids with Multi-Fidelity Datacited by 0
3 more
  1. 2026OnlyDense: Reduced-Order Modeling for Lagrangian simulationcited by 0
  2. 2026Harnessing disorder to decouple extension and shear in kirigami metamaterialscited by 0
  3. 2026pyALDIC: A Python Implementation of Augmented Lagrangian Digital Image Correlation with a GUI, Adaptive Meshing, and Mask-Aware Subset Splittingcited by 0