Cargando...
Fecha
2025-08-26
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
MDPI
Citas
Resumen
The real-time simulation of atmospheric clouds for the visualisation of outdoor scenarios has been a computer graphics research challenge since the emergence of the natural phenomena rendering field in the 1980s. In this work, we present an innovative method for real-time cumuli movement and transition based on a Recurrent Neural Network (RNN). Specifically, an LSTM, a GRU and an Elman RNN network are trained on time-series data generated by a parallel Navier–Stokes fluid solver. The training process optimizes the network to predict the velocity of cloud particles for the subsequent time step, allowing the model to act as a computationally efficient surrogate for the full physics simulation. In the experiments, we obtained natural-looking behaviour for cumuli evolution and dissipation with excellent performance by the RNN fluid algorithm compared with that of classical finite-element computational solvers. These experiments prove the suitability of
our ontogenetic computational model in terms of achieving an optimum balance between natural-looking realism and performance in opposition to computationally expensive hyper-realistic fluid dynamics simulations which are usually in non-real time. Therefore, the core contributions of our research to the state of the art in cloud dynamics are the following: a progressively improved real-time step of the RNN-LSTM fluid algorithm compared to the previous literature to date by outperforming the inference times during the runtime cumuli animation in the analysed hardware, the absence of spatial grid bounds and the replacement of fluid dynamics equation solving with the RNN. As a consequence, this method is applicable in flight simulation systems, climate awareness educational tools, atmospheric simulations, nature-based video games and architectural software.
Descripción
The registered version of this article, first published in Mathematics , is available online at the publisher's website: MDPI, https://doi.org/10.3390/math13172746
La versión registrada de este artículo, publicado por primera vez en Mathematics , está disponible en línea en el sitio web del editor: MDPI, https://doi.org/10.3390/math13172746
La versión registrada de este artículo, publicado por primera vez en Mathematics , está disponible en línea en el sitio web del editor: MDPI, https://doi.org/10.3390/math13172746
Categorías UNESCO
Palabras clave
deep recurrent neural networks, cloud dynamics (AI), natural phenomena simulation, virtual reality, computer games, volumetric rendering, digital cultural heritage
Citación
Jiménez de Parga, C.; Calo, S.; Cuadra, J.M.; García-Vico, Á.M.; Pastor Vargas, R. A Novel Method for Virtual Real-Time Cumuliform Fluid Dynamics Simulation Using Deep Recurrent Neural Networks. Mathematics 2025, 13, 2746. https:// doi.org/10.3390/math13172746
Centro
E.T.S. de Ingeniería Informática
Departamento
Sistemas de Comunicación y Control