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Physics-informed nEuRal networks for SEVERe wEather event prediction

Objective

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.

Coordinator

UNIVERSIDAD CARLOS III DE MADRID
Net EU contribution
€ 165 312,96
Address
Calle Madrid 126
28903 Getafe (Madrid)
Spain

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Region
Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Higher or Secondary Education Establishments
Other funding
No data

Partners (1)

KUNGLIGA TEKNISKA HOEGSKOLAN
Sweden
Net EU contribution
No data
Address
Brinellvagen 8
100 44 Stockholm

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Region
Östra Sverige Stockholm Stockholms län
Activity type
Higher or Secondary Education Establishments
Other funding
No data