KATO: Neural‐Reparameterized Topology Optimization Using Convolutional Kolmogorov‐Arnold Network for Stress Minimization
Bibliographic record
Abstract
ABSTRACT Topology optimization (TO) has been a cornerstone of advanced structural design for decades, yet it continues to face challenges in terms of convergence, optimality, and numerical stability, particularly for complex, non‐convex problems like stress minimization. This paper introduces a novel approach to stress‐based topology optimization through the development of neural‐reparameterized topology optimization using the convolutional Kolmogorov‐Arnold network (KATO). KATO uses the neural network to reparameterize the optimization problem, offering a unique solution to the challenges posed by stress minimization in TO. It also simplifies the penalization scheme by reducing sensitivity to certain parameters, which reduces the non‐convexity of the stress minimization problem, enhancing convergence and stability. Our method demonstrates better performance in stress minimization compared to conventional approaches and a different neural network‐based approach, achieving up to 10% lower maximum stress in common benchmark cases. KATO also shows remarkable efficiency, reducing computational time by up to 67% compared to conventional methods for stress minimization problems. We conduct a comprehensive analysis of KATO's performance, computational cost, scalability, and the impact of various neural network architectures. Our results indicate that KATO not only improves stress optimization but also offers insights into the relationship between neural network design and topology optimization performance, paving the way for more efficient and effective structural design processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".