PERSEVERE
Financiado
Physics-informed nEuRal networks for SEVERe wEather event prediction
The novel development of Physics-Informed Neural Networks (PINNs), which incorporate the constraints given by physics laws into the training process, as excellent means of computing fluidic fields and their characteristics such as...
The novel development of Physics-Informed Neural Networks (PINNs), which incorporate the constraints given by physics laws into the training process, as excellent means of computing fluidic fields and their characteristics such as velocity and pressure has open the gates to numerous applications. One of them is the data enhancement of experiments, since PINNs can reconstruct by means of applying the Navier-Stokes equations as loss function the full fluid domain in areas where experiments are limited by technology. On the other hand, the development of Generative Adversarial Networks (GANs) as robust networks with excellent precision but excessive computational costs leaves the door open to further investigate new applications where PINNs and GANs can be combined to amplify their strengths and reduce their weak points. One of those applications regards the forecast of severe weather conditions, where PINNs are useful to compute the fluidic behavior of storms approaching a certain location, whereas GANs can incorporate many additional parameters, such as wind speed, humidity, temperature and electric content, which may be essential to determine if in the following 48h a certain location is going to suffer from severe weather conditions.
The estimation and forecast of storms is essential to the air transport industry, since the losses incurred due to delays and deviations of air traffic caused by the presence of storms have been reported to be over $38.5 billion in USA. The development of a computational architecture which is able to determine if severe weather events are going to take place within the next 48h is therefore of crucial importance. There exists no model nor application in which the combined strengths of PINNs and GANs have been put into practice, one to rapidly estimate the fluidic behaviour of a moving storm, the second to calculate the properties of the field with high precision.
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Duración del proyecto: 34 meses
Fecha Inicio: 2022-07-01
Fecha Fin: 2025-05-31
Fecha Fin: 2025-05-31
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-07-01
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
HORIZON-MSCA-2021-PF-01-01:
MSCA Postdoctoral Fellowships 2021
Cerrada
hace 3 años
Presupuesto
El presupuesto total del proyecto asciende a
165K€
Líder del proyecto
UNIVERSIDAD CARLOS III DE MADRID
No se ha especificado una descripción o un objeto social para esta compañía.
Total investigadores
1332
Total investigadores
1332
N.D. |
Participante