Topology optimization is used for the design of high-performance structures but remains fundamentally limited by its iterative nature, requiring repeated finite element analyses that prevent real-time deployment and large-scale design exploration. In this work, we introduce a physics-informed transformer architecture that directly learns a non-iterative mapping from boundary conditions, loading configurations, and derived physical fields to optimized structural topologies. By leveraging global self-attention, the proposed model captures long-range mechanical interactions that govern structural response, overcoming the locality limitations of convolutional architectures. A conditioning-token mechanism embeds global problem parameters, while spatially distributed stress and strain energy fields are encoded as patch tokens within a Vision Transformer framework. To ensure physical realism and manufacturability, we incorporate auxiliary loss functions that enforce volume constraints, load adherence, and structural connectivity through a differentiable formulation. The framework is further extended to dynamic loading scenarios using frequency-domain encoding and transfer learning, enabling efficient generalization from static to time-dependent problems. Comprehensive benchmarking demonstrates that the proposed model achieves fidelity beyond that of diffusion models, while requiring only a single forward pass, thereby eliminating iterative inference entirely. This establishes topology optimization as a real-time operator-learning problem, enabling high-fidelity structural design with significant reductions in computational cost.

翻译：拓扑优化用于设计高性能结构，但其迭代特性本质上限制了其应用，需要重复进行有限元分析，从而阻碍了实时部署和大规模设计探索。本文提出了一种物理信息Transformer架构，该架构直接学习从边界条件、载荷配置及衍生物理场到优化结构拓扑的非迭代映射。通过利用全局自注意力机制，所提模型能够捕捉控制结构响应的长程力学相互作用，克服了卷积架构的局部性限制。条件令牌机制嵌入全局问题参数，而空间分布的应力和应变能场则在Vision Transformer框架中被编码为块令牌。为确保物理真实性和可制造性，我们引入了辅助损失函数，通过可微公式强制执行体积约束、载荷遵循性和结构连通性。该框架进一步通过频域编码和迁移学习扩展到动态载荷场景，实现了从静态问题到时间依赖问题的有效泛化。全面基准测试表明，所提模型实现了超越扩散模型的保真度，仅需单次前向传播，从而完全消除了迭代推断。这使拓扑优化成为一个实时算子学习问题，能够以显著降低的计算成本实现高保真结构设计。