用AI快速设计多面板复合材料结构，兼顾性能与制造可行性。
SeqGPT: A Constrained Transformer Agent for the Inverse Design of Multi-Panel Composite Structures
- 基于条件Transformer和约束束搜索，生成满足连续性要求的堆叠序列。
- 在18面板马蹄形基准上实现近实时生成，稳定性性能媲美进化算法。
- 适合需要快速优化复合材料结构的工程设计场景。
为匹配连续目标（如层合或屈曲参数）并满足离散制造约束，优化复合材料铺层序列构成一个具有挑战性的组合逆问题，尤其在使用数值优化方法（双步、双层配置）时更为显著。在多面板构型中，这种复杂性因‘拼接’——即不同面板铺层间全局兼容性/连续性要求——而进一步加剧。本文提出SeqGPT，一种用于替代计算成本高昂的迭代方法的条件Transformer代理。为从构造上保证全局连续性和制造可行性，我们采用混合神经符号解码策略：SeqGPT预测条件分布，引导受限束搜索，任何违反拼接规则的分支均被严格剪枝。在18面板马蹄形基准上的数值实验表明，SeqGPT可近乎即时生成解决方案，其屈曲性能与进化方法相当，相比现有最优方法实现显著提速。
原文摘要 · Abstract (English)
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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