mlsanityData to 7 Oct 2026

Component-wise reduced order model lattice-type structure design

arXiv 2010.10770 ↗First posted 21 Oct 2020math.NA cs.NA math.OCcited by 16

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Abstract (arXiv)

Lattice-type structures can provide a combination of stiffness with light weight that is desirable in a variety of applications. Design optimization of these structures must rely on approximations of the governing physics to render solution of a mathematical model feasible. In this paper, we propose a topology optimization (TO) formulation that approximates the governing physics using component-wise reduced order modeling, which can reduce solution time by multiple orders of magnitude over a full-order finite element model while providing a relative error in the solution of less than one percent. In addition, the offline training data set from such component-wise models is reusable, allowing its application to many design problems for only the cost of a single offline training phase, and the component-wise method is nearly embarrassingly parallel. We also show how the parameterization chosen in our optimization allows a simplification of the component-wise reduced order model (CWROM) not noted in previous literature, for further speedup of the optimization process. The sensitivity of the compliance with respect to the particular parameterization is derived solely in the component level. In numerical examples, we demonstrate a 1000x speedup over a full-order FEM model with relative error of less than one percent and show minimum compliance designs for two different cantilever beam examples, one smaller and one larger. Finally, error bounds for displacement field, compliance, and compliance sensitivity of the CWROM are derived.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Oct 2020.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 1; 2025: 6; 2026: 1.

16 papers.

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  1. 2021A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networkscited by 30
  2. 2022gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identificationcited by 30
  3. 2023GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencodercited by 29
  4. 2021Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniquescited by 23
  5. 2025Defining Foundation Models for Computational Science: A Call for Clarity and Rigorcited by 16
  6. 2024Physics-informed active learning with simultaneous weak-form latent space dynamics identificationcited by 13
  7. 2025Thermodynamically Consistent Latent Dynamics Identification for Parametric Systemscited by 8
  8. 2023Reduced-order modeling for parameterized PDEs via implicit neural representationscited by 7
  9. 2022Certified data-driven physics-informed greedy auto-encoder simulatorcited by 4
  10. 2022Machine Learning in Heterogeneous Porous Materialscited by 3
  11. 2025mLaSDI: Multi-stage latent space dynamics identificationcited by 2
  12. 2025Sequential decoder training for improved latent space dynamics identificationcited by 2
4 more
  1. 2025Higher-Order LaSDI: Reduced Order Modeling with Multiple Time Derivativescited by 2
  2. 2026Latent Space Element Methodcited by 1
  3. 2023Efficient machine-learning surrogates for large-scale geological carbon and energy storagecited by 0
  4. 2025Reinforcement Learning for Accelerated Aerodynamic Shape Optimisationcited by 0