预测瞬态城市风对于理解城市微气候和设计气候韧性城市至关重要。建筑解析大涡模拟（Building-resolving large-eddy simulation）能够生成详细的不可压缩城市风场，但针对每种布局的计算成本高昂。神经代理模型通过学习速度场的演化提供了一种更快的替代方案。然而，最小化速度预测误差并不能保证局部质量守恒和壁面不可渗透性，而这两者共同定义了运动学可容许性。这一局限性源于无约束的输出表示：几何条件引导了预测，但未将预测限制在可容许的速度场内。纠正这些输出中的边界违反会改变相邻流体单元的通量平衡，从而可能损害局部质量守恒。为解决这一挑战，我们提出了 Sculpt，一种嵌套势函数框架，它将耦合的、依赖于几何的约束直接构建在其参数化中。这种嵌套参数化通过在原生三维交错网格上对体积矢量势进行离散旋度运算，生成无散度的速度更新。一个共享标量势约束矢量势的边界值，使得同一算子也能强制不可渗透性，而无需逐步压力投影。由于通过该旋度进行反向传播会衰减大尺度梯度信号，我们在多个分辨率下参数化体积势，以更好地捕捉大尺度流动结构。我们引入了 UrbanWindFlow，这是一个涵盖多种城市形态和入流条件的大涡模拟数据集，用于同时评估准确性和运动学可容许性。

Predicting transient urban winds is fundamental to understanding urban microclimates and designing climate-resilient cities. Building-resolving large-eddy simulation produces detailed incompressible urban wind fields at substantial computational cost for each layout. Neural surrogates offer a faster alternative by learning to predict the evolution of velocity fields. However, minimizing velocity prediction error does not guarantee local mass conservation and wall impermeability, which together define kinematic admissibility. This limitation stems from an unconstrained output representation: geometry conditioning guides predictions but does not restrict them to admissible velocity fields. Correcting boundary violations in these outputs changes the flux balance in adjacent fluid cells and may consequently compromise local mass conservation. To address the challenge, we propose Sculpt, a nested potential framework that builds the coupled, geometry-dependent constraints directly into its parameterization. This nested parameterization generates divergence-free velocity updates through the discrete curl of a volume vector potential on the native three-dimensional staggered grid. A shared scalar potential constrains the vector potential's boundary values so that the same operator also enforces impermeability, without a per-step pressure projection. Because backpropagation through this curl attenuates large-scale gradient signals, we parameterize the volume potential at multiple resolutions to better capture large-scale flow structures. We introduce UrbanWindFlow, an LES dataset spanning urban morphologies and inflow conditions, to evaluate accuracy and kinematic admissibility together.