mlsanityData to 7 Oct 2026

Neural networks for topology optimization

arXiv 1709.09578 ↗First posted 27 Sep 2017cs.LG cs.NA math.NAcited by 39

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Abstract (arXiv)

In this research, we propose a deep learning based approach for speeding up the topology optimization methods. The problem we seek to solve is the layout problem. The main novelty of this work is to state the problem as an image segmentation task. We leverage the power of deep learning methods as the efficient pixel-wise image labeling technique to perform the topology optimization. We introduce convolutional encoder-decoder architecture and the overall approach of solving the above-described problem with high performance. The conducted experiments demonstrate the significant acceleration of the optimization process. The proposed approach has excellent generalization properties. We demonstrate the ability of the application of the proposed model to other problems. The successful results, as well as the drawbacks of the current method, are discussed.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Sep 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 8; 2023: 8; 2024: 5; 2025: 2; 2026: 8.

39 papers.

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  1. 2021Deep Generative Models in Engineering Design: A Reviewcited by 103
  2. 2022Diffusion Models Beat GANs on Topology Optimizationcited by 79
  3. 2021High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learningcited by 54
  4. 2023Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generationcited by 51
  5. 2021BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarkscited by 27
  6. 2022Topology Optimization via Machine Learning and Deep Learning: A Reviewcited by 22
  7. 2023Deep Learning in Deterministic Computational Mechanicscited by 21
  8. 2024Large Language Model Agent as a Mechanical Designercited by 20
  9. 2022Modular machine learning-based elastoplasticity: generalization in the context of limited datacited by 16
  10. 2023PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process Guidancecited by 9
  11. 2024Neural topology optimization: the good, the bad, and the uglycited by 9
  12. 2023Differentiable Visual Computing for Inverse Problems and Machine Learningcited by 7
27 more
  1. 2025EngiBench: A Framework for Data-Driven Engineering Design Researchcited by 6
  2. 2023High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Samplingcited by 5
  3. 2023Latent Diffusion Models for Structural Component Designcited by 4
  4. 2026On the Generalization in Topology Optimization via Sensitivity-Conditioned Bernoulli Flow Matchingcited by 4
  5. 2023iPINNs: Incremental learning for Physics-informed neural networkscited by 3
  6. 2024NITO: Neural Implicit Fields for Resolution-free Topology Optimizationcited by 3
  7. 2021Speeding up Computational Morphogenesis with Online Neural Synthetic Gradientscited by 2
  8. 2021DNN-Based Topology Optimisation: Spatial Invariance and Neural Tangent Kernelcited by 2
  9. 2024Gradient-free neural topology optimization: Towards effective fracture-resistant designscited by 2
  10. 2024Inverse design with conditional cascaded diffusion modelscited by 2
  11. 2021GANTL: Towards Practical and Real-Time Topology Optimization with Conditional GANs and Transfer Learningcited by 1
  12. 2021Robust Topology Optimization Using Multi-Fidelity Variational Autoencoderscited by 1
  13. 2022An Adaptive and Scalable ANN-based Model-Order-Reduction Method for Large-Scale TO Designscited by 1
  14. 2026Large Language Models as Optimization Controllers: Adaptive Continuation for SIMP Topology Optimizationcited by 1
  15. 2026Trajectory-Aware Flow Matching for Topology Optimisationcited by 1
  16. 2021A mechanistic-based data-driven approach to accelerate structural topology optimization through finite element convolutional neural network (FE-CNN)cited by 0
  17. 2022Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networkscited by 0
  18. 2022FRC-TOuNN: Topology Optimization of Continuous Fiber Reinforced Composites using Neural Networkcited by 0
  19. 2022Faster Optimization on Sparse Graphs via Neural Reparametrizationcited by 0
  20. 2022SELTO: Sample-Efficient Learned Topology Optimizationcited by 0
  21. 2023A Meta-Generation framework for Industrial System Generationcited by 0
  22. 2025Diverse Topology Optimization using Modulated Neural Fieldscited by 0
  23. 2026GenPANIS: A Latent-Variable Generative Framework for Forward and Inverse PDE Problems in Multiphase Mediacited by 0
  24. 2026AutoSiMP: Autonomous Topology Optimization from Natural Language via LLM-Driven Problem Configuration and Adaptive Solver Controlcited by 0
  25. 2026Adversarial Agents on Topology Optimization: Understanding the Fragility and Robustness of Deep Learning-based and Physics-Based Design Models under Adversarial Perturbationcited by 0
  26. 2026HGTO: A Unified Graph-Based Physics-Informed Formulation for Structural Topology Optimizationcited by 0
  27. 2026TopoMamba: A Load-Support Relation-Guided Multi-Directional State-Space Model for Topology Optimizationcited by 0