mlsanityData to 14 Oct 2026

Deep Learning in Deterministic Computational Mechanics

arXiv 2309.15421 ↗First posted 27 Sep 2023cs.LGcited by 21

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Abstract (arXiv)

The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies within this field, we provide an overview of deep learning in deterministic computational mechanics. Five main categories are identified and explored: simulation substitution, simulation enhancement, discretizations as neural networks, generative approaches, and deep reinforcement learning. This review focuses on deep learning methods rather than applications for computational mechanics, thereby enabling researchers to explore this field more effectively. As such, the review is not necessarily aimed at researchers with extensive knowledge of deep learning -- instead, the primary audience is researchers at the verge of entering this field or those who attempt to gain an overview of deep learning in computational mechanics. The discussed concepts are, therefore, explained as simple as possible.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 237 of its 640 references that are in the map. First posted on arXiv 27 Sep 2023.

Builds on

237 of 640 references.

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Citing papers per year: 2024: 7; 2025: 8; 2026: 6.

21 papers.

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  2. 2024BiLO: Bilevel Local Operator Learning for PDE Inverse Problemscited by 8
  3. 2024An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learningcited by 6
  4. 2024Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materialscited by 5
  5. 2024Neural Networks for Generating Better Local Optima in Topology Optimizationcited by 4
  6. 2026A holomorphic neural network framework for 3D boundary value problems governed by harmonic potentialscited by 2
  7. 2024Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from Engineering Designcited by 1
  8. 2025Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Formingcited by 1
  9. 2025Information Security Based on LLM Approaches: A Reviewcited by 1
  10. 2025Graph Network-based Structural Simulator: Graph Neural Networks for Structural Dynamicscited by 1
  11. 2024Robustness investigation of cross-validation based quality measures for model assessmentcited by 0
  12. 2025CNN-powered micro- to macro-scale flow modeling in deformable porous mediacited by 0
9 more
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  2. 2025Trustworthy AI-based crack-tip segmentation using domain-guided explanationscited by 0
  3. 2025Enhancing material behavior discovery using embedding-oriented Physically-Guided Neural Networks with Internal Variablescited by 0
  4. 2025A Comparison of Surrogate Constitutive Models for Viscoplastic Creep Simulation of HT-9 Steelcited by 0
  5. 2026Contactless estimation of continuum displacement and mechanical compressibility from image series using a deep learning based frameworkcited by 0
  6. 2026Non-Invasive Reconstruction of Cardiac Activation Dynamics Using Physics-Informed Neural Networkscited by 0
  7. 2026An ALE-Consistent Graph Neural Operator-Transformer Framework for Fluid-Structure Interactioncited by 0
  8. 2026Adaptive Distance-Aware Trunk Deep Operator Learning for Long-Span Roadway Bridgescited by 0
  9. 2026HyperShape: Hyperelasticity Across Diverse Shapescited by 0