arXiv:2603. 21210v3 Announce Type: replace Abstract: Designing urban spaces that provide pedestrian wind comfort and safety requires time-resolved Computational Fluid Dynamics (CFD) simulations, but their current computational cost makes extensive design exploration impractical.
arXiv:2608.03323v2 Announce Type: replace Abstract: Estimating room layouts from multi-view imagery is a core task for indoor scene understanding. Existing methods are typically limited either by poo...
Fast Localized Urban Microclimate Emulator (FLUME) is a rapid, data‑efficient framework that predicts three‑dimensional wind and temperature fields in urban environments using a Fourier neural operator. It incorporates a Multi‑Directional Distance Feature (MDDF) to capture building geometry effects and is trained on CFD simulations of only 23 urban blocks, achieving mean absolute errors of 0.2 m/s for velocity and 0.19 °C for temperature on unseen configurations. An ensemble approach provides epistemic uncertainty estimates, with higher uncertainty in roof‑level shear layers, building wakes, and near‑ground vertical velocity.
arXiv:2512. 16275v2 Announce Type: replace-cross Abstract: Automated floor plan generation lies at the intersection of combinatorial search, geometric constraint satisfaction, and functional design requirements -- a confluence that has historically resisted a unified computational treatment.
arXiv:2610.18511v1 Announce Type: new Abstract: Buildings cause nearly 40\% of carbon emissions in the US. Of total building emissions, HVAC systems are responsible for approximately 40-60\% and acco...
The paper introduces the Estimator from Scratch, a neural network that embeds RC thermal building equations to estimate parameters, and its extension, the Pretrained Estimator, which is pretrained on multiple buildings to avoid initial‑guess dependence. Both methods outperform a genetic‑algorithm estimator and a fully black‑box neural network across simulated and real buildings, achieving superior prediction accuracy and lower, more consistent MPC costs. The Pretrained Estimator is highlighted as a robust, computationally efficient, and initial‑guess‑free alternative for RC parameter estimation, with potential applicability to other control‑oriented dynamical systems.
The paper presents a semi‑automated pipeline that transforms a single 360‑degree video of a classroom into editable, simulation‑ready 3D geometry for CFD analysis. Using NeRF for dense point clouds, SAM‑based 2D instance masks lifted to 3D, and octree‑based instance separation, the workflow produces object‑level assets that can be reconfigured in a browser editor. The resulting geometry is validated with OpenFOAM simulations and benchmarked against IEA Annex 20, demonstrating that per‑room geometry acquisition is essential for accurate airflow predictions.
arXiv:2606. 04658v1 Announce Type: cross Abstract: Optimizing urban layouts for climate adaptation requires balancing building density with cold-air ventilation.
arXiv:2607. 11672v1 Announce Type: new Abstract: Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur substantial computational costs.
arXiv:2608. 06772v1 Announce Type: new Abstract: Accurate estimation of building energy use is essential for achieving carbon neutral and sustainable buildings.
arXiv:2601. 18707v2 Announce Type: replace-cross Abstract: Machine learning-based surrogate models have emerged as more efficient alternatives to numerical solvers for physical simulations over complex geometries, such as car bodies.
arXiv:2609.17160v1 Announce Type: new Abstract: Machine-learning surrogate models offer a promising alternative to high-fidelity Computational Fluid Dynamics (CFD) simulations for aerodynamic analysi...