A-MFO-Net: Boundary-Conditioned GNN–FNO–GNN Operators for Long-Horizon Room-Scale 3D Thermal-Fluid Simulation

Long-horizon three-dimensional thermal-fluid simulation on unstructured meshes requires both global information propagation and geometry-sensitive local recon- struction. We introduce A-MFO-Net, a boundary-conditioned GNN–FNO–GNN operator whose graph stages weight edge-conditioned messages using source and destination states and directed edge geometry. On a held-out 22,000-cell extended- room data-center trajectory (90% ACU cooling, 100% server workload), we predict five state channels over 400 steps and 20 seconds. Training is predominantly on 200-step room-scale trajectories, with one shorter extended-room and one longer room-scale case, so the test probes their joint transfer rather than isolated geometry or horizon extrapolation. All seven models use 200 training windows per epoch and seeds 42, 43, and 44. At the final step, A-MFO-Net’s relative-L2 errors (mean ± sample standard deviation) are (6.97 ± 0.18)% for velocity, (31.56 ± 4.75)% for pressure, and (5.05 ± 0.15)% for temperature: 68.6% and 22.8% below the strongest external velocity and pressure comparators, respectively. A-MFO-Net has the lowest mean velocity and pressure errors across all seven models, while GAOT has the lowest mean temperature error. FNO has (39.24 ± 0.99)% velocity error and highly variable pressure error, supporting the implemented graph-containing pipeline but not isolating attention or neighbor aggregation. A-MFO-Net completes the 400-step evaluation in 64.0 ± 1.3 seconds.