mlsanityData to 14 Oct 2026

Inverse-design of nonlinear mechanical metamaterials via video denoising diffusion models

arXiv 2305.19836 ↗First posted 31 May 2023cs.CEcited by 48

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Abstract (arXiv)

The accelerated inverse design of complex material properties - such as identifying a material with a given stress-strain response over a nonlinear deformation path - holds great potential for addressing challenges from soft robotics to biomedical implants and impact mitigation. While machine learning models have provided such inverse mappings, they are typically restricted to linear target properties such as stiffness. To tailor the nonlinear response, we here show that video diffusion generative models trained on full-field data of periodic stochastic cellular structures can successfully predict and tune their nonlinear deformation and stress response under compression in the large-strain regime, including buckling and contact. Unlike commonly encountered black-box models, our framework intrinsically provides an estimate of the expected deformation path, including the full-field internal stress distribution closely agreeing with finite element simulations. This work has thus the potential to simplify and accelerate the identification of materials with complex target performance.

Key facts. Cited by 48 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 May 2023.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 15; 2025: 17; 2026: 15.

48 papers.

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  1. 2024Physics-Informed Diffusion Modelscited by 90
  2. 2024An operator learning perspective on parameter-to-observable mapscited by 22
  3. 2025Training-Free Constrained Generation With Stable Diffusion Modelscited by 20
  4. 2024State-observation augmented diffusion model for nonlinear assimilation with unknown dynamicscited by 8
  5. 2024Conditional score-based diffusion models for solving inverse problems in mechanicscited by 6
  6. 2024Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materialscited by 5
  7. 2025DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modelingcited by 5
  8. 2025Algebraic Language Models for Inverse Design of Metamaterials via Diffusion Transformerscited by 5
  9. 2024Neural Networks for Generating Better Local Optima in Topology Optimizationcited by 4
  10. 2024Nonlinear Inverse Design of Mechanical Multi-Material Metamaterials Enabled by Video Denoising Diffusion and Structure Identifiercited by 4
  11. 2025MIND: Microstructure INverse Design with Generative Hybrid Neural Representationcited by 4
  12. 2023Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learningcited by 3
36 more
  1. 20253DID: Direct 3D Inverse Design for Aerodynamics with Physics-Aware Optimizationcited by 3
  2. 2024LatticeGraphNet: A two-scale graph neural operator for simulating lattice structurescited by 2
  3. 2024An Uncertainty-aware Deep Learning Framework-based Robust Design Optimization of Metamaterial Unitscited by 2
  4. 2024FFT-based surrogate modeling of auxetic metamaterials with real-time prediction of effective elastic properties and swift inverse designcited by 2
  5. 2025MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discoverycited by 2
  6. 2025UniMate: A Unified Model for Mechanical Metamaterial Generation, Property Prediction, and Condition Confirmationcited by 2
  7. 2025Single vs. Multiple Branches in DeepONet and S-DeepONet: Network Architecture Follows Coupling in Multiphysics Systemscited by 2
  8. 2026Geometry-Aware Neural Optimizer for Shape Optimization and Inversioncited by 2
  9. 2024Inverse designing metamaterials with programmable nonlinear functional responses in graph spacecited by 1
  10. 2024Univariate Conditional Variational Autoencoder for Morphogenic Patterns Design in Frontal Polymerization-Based Manufacturingcited by 1
  11. 2025Reduced-order structure-property linkages for stochastic metamaterialscited by 1
  12. 2025Friction on Demand: A Generative Framework for the Inverse Design of Metainterfacescited by 1
  13. 2026A Hybrid Conditional Diffusion-DeepONet Framework for High-Fidelity Stress Prediction in Hyperelastic Materialscited by 1
  14. 2026AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Designcited by 1
  15. 2026Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospectscited by 1
  16. 2024Learning Physics-Consistent Material Behavior from Dynamic Displacementscited by 0
  17. 2024Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Spacecited by 0
  18. 2024AI-driven Inverse Design of Band-Tunable Mechanical Metastructures for Tailored Vibration Mitigationcited by 0
  19. 2025Universal Physics Simulation: A Foundational Diffusion Approachcited by 0
  20. 2025Denoising diffusion models for inverse design of inflatable structures with programmable deformationscited by 0
  21. 2025GeoFunFlow: Geometric Function Flow Matching for Inverse Operator Learning over Complex Geometriescited by 0
  22. 2025Generative Models for Helmholtz Equation Solutions: A Dataset of Acoustic Materialscited by 0
  23. 2025Robust inverse material design with physical guarantees using the Voigt-Reuss Netcited by 0
  24. 2025Towards Overcoming Data Scarcity in Nuclear Energy: A Study on Critical Heat Flux with Physics-consistent Conditional Diffusion Modelcited by 0
  25. 2025PIS: A Generalized Physical Inversion Solver for Arbitrary Sparse Observations via Set Conditioned Flow Matchingcited by 0
  26. 2026RAG: A Random-Forest-Based Generative Design Framework for Uncertainty-Aware Design of Metamaterials with Complex Functional Response Requirementscited by 0
  27. 2026Guided Diffusion by Optimized Loss Functions on Relaxed Parameters for Inverse Material Designcited by 0
  28. 2026Data-efficient Bayesian-guided design selection from large candidate sets: Application to hyperelastic stochastic metamaterialscited by 0
  29. 2026Climate Prompting: Generating the Madden-Julian Oscillation using Video Diffusion and Low-Dimensional Conditioningcited by 0
  30. 2026Reinforcement learning for inverse silhouette design and rapid laser cutting of compact parallelogram quad kirigami prototypescited by 0
  31. 2026Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstructioncited by 0
  32. 2026Solving Inverse Problems of Chaotic Systems with Bidirectional Conditional Flow Matchingcited by 0
  33. 2026CertMix: Certified, Data-Efficient Metamaterial Design by Affine Mixing of Aligned Neural-Implicit Weight Spacescited by 0
  34. 2026One-Shot Generative Design for Disordered Metamaterials via Self-Organizing Neural Cellular Automatacited by 0
  35. 2026Harnessing disorder to decouple extension and shear in kirigami metamaterialscited by 0
  36. 2026Growth-Inspired Graph Generation and Inverse Design of Mechanical Lattices via Dot Matrices Database Augmentation and GCNNcited by 0