SeqGPT：用于多面板复合结构逆向设计的受限Transformer智能体
SeqGPT: A Constrained Transformer Agent for the Inverse Design of Multi-Panel Composite Structures
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中文总结 AI 辅助
研究针对多面板复合结构逆向设计的组合逆问题，提出SeqGPT智能体，采用混合神经符号解码策略，经实验验证其能快速生成与进化方法性能相当的解决方案，加速了设计过程。
中文摘要 AI 辅助
优化复合材料铺层序列，使其在离散制造约束下匹配连续目标（如层合或屈曲参数），是复合材料设计中具有挑战性的组合逆问题，在多面板配置中，不同面板铺层间的全局兼容性/连续性要求会进一步加剧这种复杂性。本研究提出SeqGPT，一种用于取代计算昂贵的迭代方法的条件Transformer智能体。通过构建混合神经符号解码策略确保全局连续性和制造可行性。SeqGPT预测条件分布以指导受限束搜索，违反混合规则的分支将被严格修剪。在18面板马蹄形基准上的数值实验表明，SeqGPT能近乎即时生成解决方案，屈曲性能与进化方法相当，相比现有技术有显著加速。
英文摘要
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.
