Computer-aided engineering (CAE) is shifting from human-driven workflows toward AI-driven ones, including physics foundation models that generalize across geometries and operating conditions. Unlike LLMs, these models depend on large volumes of high-fidelity, physics-compliant data.  Recent scaling-law work on computational fluid dynamics (CFD) surrogates indicates that simulation-generated training data is often the limiting cost in practice. This pushes requirements onto the simulator, which must be GPU-native, fast, and able to plug directly into ML workflows. NVIDIA Warp is a framework for accelerated simulation, data generation, and spatial computing that bridges CUDA and Python. Warp enables developers to write high-performance kernels as regular Python functions that are JIT-compiled into efficient code for execution on the GPU.…