SeqGPT: A Constrained Transformer Agent for the Inverse Designof Multi-Panel Composite Structures

• URL: http://arxiv.org/abs/2607.11910v1
• Date: Fri, 03 Jul 2026 12:00:39 GMT
• Title: SeqGPT: A Constrained Transformer Agent for the Inverse Designof Multi-Panel Composite Structures
• Abstract summary: SeqGPT is a conditional Transformer agent developed to replace computationally expensive iterative methods.<n> Numerical experiments on the 18-panel horseshoe benchmark demonstrate that SeqGPT generates solutions near-instantaneously.
• Score: 43.7610048074368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimizing composite stacking sequences to match continuous targets (e.g., Lamination or Buckling Parameters) with discrete manufacturing constraints represents a challenging combinatorial inverse problem that regularly occurs in composite design especially when numerical optimization approaches are used (bi-step, bi-level configurations). In multipanel configurations, this complexity is further intensified by blending, a global compatibility/continuity requirement between the different panel stackings. This study presents SeqGPT, a conditional Transformer agent developed to replace computationally expensive iterative methods. To ensure both global continuity and manufacturing feasibility by construction, we implemented a hybrid neurosymbolic decoding strategy. SeqGPT predicts a conditional distribution that guides a Constrained Beam Search, where any branch violating blending rules is strictly pruned. Numerical experiments on the 18-panel horseshoe benchmark demonstrate that SeqGPT generates solutions near-instantaneously with buckling performance comparable to evolutionary methods, offering a significant speed-up compared to the state of the art.

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