# A Multimodal Physics-Informed Neural Network Approach for Mean Radiant Temperature Modeling

arXiv: https://arxiv.org/abs/2503.08482 · HTML: https://mlsanity.com/paper/2503.08482/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Characterizing possible failure modes in physics-informed neural networks](https://mlsanity.com/paper/2109.01050/) (2021)
- [Physics-informed neural networks (PINNs) for fluid mechanics: A review](https://mlsanity.com/paper/2105.09506/) (2021)
- [Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations](https://mlsanity.com/paper/1711.10561/) (2017)
- [Deep Generative Data Assimilation in Multimodal Setting](https://mlsanity.com/paper/2404.06665/) (2024)
- [deepFDEnet: A Novel Neural Network Architecture for Solving Fractional Differential Equations](https://mlsanity.com/paper/2309.07684/) (2023)
- [Solving Falkner-Skan type equations via Legendre and Chebyshev Neural Blocks](https://mlsanity.com/paper/2308.03337/) (2023)
- [PINNIES: An Efficient Physics-Informed Neural Network Framework to Integral Operator Problems](https://mlsanity.com/paper/2409.01899/) (2024)
- [CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments](https://mlsanity.com/paper/2410.03211/) (2024)

## Built on by (most cited first, 6 of 6)

- [MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection](https://mlsanity.com/paper/2505.11416/) (2025)
- [MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory](https://mlsanity.com/paper/2507.11821/) (2025)
- [Explainable Human-in-the-Loop Segmentation via Critic Feedback Signals](https://mlsanity.com/paper/2510.09945/) (2025)
- [Physics-Informed Modeling for Wood Thermal Analysis and Prediction](https://mlsanity.com/paper/2606.23402/) (2026)
- [Tri-Accel: Curvature-Aware Precision-Adaptive and Memory-Elastic Optimization for Efficient GPU Usage](https://mlsanity.com/paper/2508.16905/) (2025)
- [From Hubs to Deserts: Urban Cultural Accessibility Patterns with Explainable AI](https://mlsanity.com/paper/2511.07475/) (2025)

## Abstract (arXiv)

Outdoor thermal comfort is a critical determinant of urban livability, particularly in hot desert climates where extreme heat poses challenges to public health, energy consumption, and urban planning. Mean Radiant Temperature (Tmrt) is a key parameter for evaluating outdoor thermal comfort, especially in urban environments where radiation dynamics significantly impact human thermal exposure. Traditional methods of estimating Tmrt rely on field measurements and computational simulations, both of which are resource intensive. This study introduces a Physics-Informed Neural Network (PINN) approach that integrates shortwave and longwave radiation modeling with deep learning techniques. By leveraging a multimodal dataset that includes meteorological data, built environment characteristics, and fisheye image-derived shading information, our model enhances predictive accuracy while maintaining physical consistency. Our experimental results demonstrate that the proposed PINN framework outperforms conventional deep learning models, with the best-performing configurations achieving an RMSE of 3.50 and an R2 of 0.88. This approach highlights the potential of physics-informed machine learning in bridging the gap between computational modeling and real-world applications, offering a scalable and interpretable solution for urban thermal comfort assessments.