# Operator learning for energy-efficient building ventilation control with computational fluid dynamics simulation of a real-world classroom

arXiv: https://arxiv.org/abs/2504.21243 · HTML: https://mlsanity.com/paper/2504.21243/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Fourier Neural Operator for Parametric Partial Differential Equations](https://mlsanity.com/paper/2010.08895/) (2020)
- [DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators](https://mlsanity.com/paper/1910.03193/) (2019)
- [GNOT: A General Neural Operator Transformer for Operator Learning](https://mlsanity.com/paper/2302.14376/) (2023)
- [Geometry-Informed Neural Operator for Large-Scale 3D PDEs](https://mlsanity.com/paper/2309.00583/) (2023)
- [Optimal Control Via Neural Networks: A Convex Approach](https://mlsanity.com/paper/1805.11835/) (2018)
- [Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid Simulations](https://mlsanity.com/paper/2107.04556/) (2021)
- [Modeling and Optimization of Complex Building Energy Systems with Deep Neural Networks](https://mlsanity.com/paper/1711.02278/) (2017)
- [Ventilation and Temperature Control for Energy-efficient and Healthy Buildings: A Differentiable PDE Approach](https://mlsanity.com/paper/2403.08996/) (2024)
- [Modeling and Estimation of the Humans' Effect on the CO2 Dynamics Inside a Conference Room](https://mlsanity.com/paper/1403.5085/) (2014)
- [Data-driven control of COVID-19 in buildings: a reinforcement-learning approach](https://mlsanity.com/paper/2212.13559/) (2022)

## Built on by (most cited first, 1 of 1)

- [Drifting Models for Surrogate Flow Modeling](https://mlsanity.com/paper/2606.07481/) (2026)

## Abstract (arXiv)

Energy-efficient ventilation control plays a vital role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD) simulations provide detailed and physically accurate representation of indoor airflow, their high computational cost limits their use in real-time building control. In this work, we present a neural operator learning framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable building ventilation control with the high-fidelity fluid dynamics models. Our method jointly optimizes the airflow supply rates and vent angles to reduce energy use and adhere to air quality constraints. We train an ensemble of neural operator transformer models to learn the mapping from building control actions to airflow fields using high-resolution CFD data. This learned neural operator is then embedded in an optimization-based control framework for building ventilation control. Experimental results show that our approach achieves significant energy savings compared to maximum airflow rate control, rule-based control, as well as data-driven control methods using spatially averaged CO2 prediction and deep learning based reduced order model, while consistently maintaining safe indoor air quality. These results highlight the practicality and scalability of our method in maintaining energy efficiency and indoor air quality in real-world buildings.