FluidTopoBench: A Benchmark for Learning-Based Fluid Topology Optimization

Learning-based methods are increasingly used in fluid topology optimization to generate channel layouts, predict flow responses, and initialize numerical solvers. However, these capabilities are typically evaluated in isolation, making it difficult to assess how learned outputs translate into physical design quality and downstream optimization performance. We introduce FluidTopoBench, a benchmark of 40,500 steady two-dimensional incompressible Navier–Stokes–Brinkman problems spanning diverse channel layouts, flow regimes, and obstacle configurations. Each instance pairs operating conditions and the response of a uniform initial design with a binary reference topology, its finite-element response and dissipation, and numerical records for downstream evaluation. FluidTopoBench supports topology generation, response prediction, candidate selection, and warm-start optimization under a shared physical evaluation protocol. We evaluate six generators and six predictors using geometric, physical, and downstream metrics across multiple physical and geometric variants. Initial-response conditioning lowers mean dissipation error under unseen boundary layouts, while frozen response predictors provide useful signals for finite-element-verified candidate selection. Generated candidate sets can contain designs with lower dissipation than the numerical reference, and suitable initialization processing improves optimization endpoints. Finally, FluidTopoBench provides a unified framework for evaluating learned geometries and responses by their verified design and optimization outcomes.