aiqbench analysis
The paper proposes a graph neural network that adds a discretized advection-diffusion equation as a soft loss penalty, aiming to forecast transport processes on irregular sensor networks. Its evaluation is on par with existing baselines rather than clearly superior, and the solar PV gain is shown for intra-day forecasting only. Readers should check how the physics term is weighted and how it generalizes beyond the reported datasets.
PING: physics informed graph neural network for advection-diffusion processes with applications to solar power forecasting
arXiv:2610.18280v1 Announce Type: new Abstract: Advection-diffusion plays an essential role in transport phenomena such as atmospheric and cloud dynamics. Improved forecasting of cloud motion, in turn, enhances solar power production forecasts and facilitates the integration of additional photovoltaic (PV) sources into the power grid. However, both classical numerical models and emerging deep learning approaches face challenges when applied to spatially irregular sensor networks such as those formed by weather stations. In this work, we adopt a graph signal processing perspective and model advection-diffusion particles as signals on a graph to capture the dynamics of these processes. We introduce a Physics-Informed Graph Neural Network (PING) for forecasting future advection-diffusion particle concentrations on irregular grids. The model incorporates a discretized advection-diffusion equation as a soft constraint in the loss function to guide learning toward physically consistent solutions. This model is on par with state of the art models on synthetic and real transport phenomena datasets, including sea surface temperature and cloud index datasets. Furthermore, the PING model is successfully coupled with models for forecasting PV power production. This hybrid approach leverages data fusion from ground-based sensors at PV stations and satellite images with cloud concentration to improve the accuracy of intra-day PV power production.
Abstract:Advection-diffusion plays an essential role in transport phenomena such as atmospheric and cloud dynamics. Improved forecasting of cloud motion, in turn, enhances solar power production forecasts and facilitates the integration of additional photovoltaic (PV) sources into the power grid. However, both classical numerical models and emerging deep learning approaches face challenges when applied to spatially irregular sensor networks such as those formed by weather stations. In this work, we adopt a graph signal processing perspective and model advection-diffusion particles as signals on a graph to capture the dynamics of these processes. We introduce a Physics-Informed Graph Neural Network (PING) for forecasting future advection-diffusion particle concentrations on irregular grids. The model incorporates a discretized advection-diffusion equation as a soft constraint in the loss function to guide learning toward physically consistent solutions. This model is on par with state of the art models on synthetic and real transport phenomena datasets, including sea surface temperature and cloud index datasets. Furthermore, the PING model is successfully coupled with models for forecasting PV power production. This hybrid approach leverages data fusion from ground-based sensors at PV stations and satellite images with cloud concentration to improve the accuracy of intra-day PV power production.
Machine Learning (cs.LG); Signal Processing (eess.SP)
arXiv:2610.18280 [cs.LG]
(or arXiv:2610.18280v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.18280 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 294 words
Related coverage
Korzhinskii-Net: Physics-Informed Neural Network for Sub-Surface Mineral Prospectivity Modelling
arXiv · Jun 15, 2026
Hybrid coupling with numerics-informed neural networks and the overlapping Schwarz alternating method
arXiv · Sep 17, 2026
MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science
arXiv · Jun 9, 2026
Reliable mechanistic operator recovery with biologically-informed neural networks: principles for architecture and optimisation design
arXiv · Oct 5, 2026