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Generative Design for Early-Stage Site Feasibility: Past Projects as Warm Starts, and Where AI Fits

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GD–AI Loop 2.0 · Lab Notes #7 · From testbed to practice The notes in this series up to Lab Notes #6 came from a simplified 1.5 m grid testbed. This one asks: “So what is it good for in actual design practice?” What was confirmed in the testbed and how it might carry over to practice (not yet validated) are separated by color. We then fed a single brief — “10 rooms, 2 floors” — into a fictional irregular lot and ran it end to end as one flow: from the instant answer, to a GA warm-started from past designs (here, solutions this pipeline had already produced for other sites, standing in for an office’s past projects), to a comparison of egress evaluation criteria. After seeing those results, we added one wider lot, with room to choose exit stairs, as an example. Nov 1, 2026 (English edition Nov 6) · Previous: Lab Notes #6 — evacuation simulation as the fitness function Update, Nov 6, 2026. Takeaway ③ and §5 compare evacuation criteria with the v1 crowd model of Lab Notes #6, which h...

Verified GPU Crowd Evacuation Simulation for Generative Design: IMO Tests, SFPE Flows, and 120 Buildings in NVIDIA Omniverse

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GD–AI Loop 2.0 · Lab Notes #8 · verified crowd model → 20,947 buildings → AI An earlier, unpublished draft of this post trained a neural network on the crowd model of Lab Notes #6 — a model that a fire engineer would call a toy: one person per 1.5 m cell, so the densest crowd it could form was 0.44 persons/m²; doors one cell wide; single-file stairs; no checks against standard test cases; three example buildings. This post replaces the people model (the old model is called v1 below, the new one v7) and shows the checks a practitioner would ask for before trusting any number — the IMO MSC.1/Circ.1533 test cases, SFPE flows and speed–density, and the same model run in JuPedSim. It then re-runs all 20,947 designs with occupant loads, pre-movement times and blocked-exit and blocked-stair scenarios, stages 120 of them at once in NVIDIA Omniverse, re-measures the Lab Notes #6 result, and retrains the network. Nov 6, 2026 · rewritten after a practitioner's critique of the earlier draft...