Descripción del proyecto
Una nueva forma de calcular el comportamiento de las tormentas
La predicción de fenómenos meteorológicos extremos, como fuertes lluvias y vendavales, es importante porque da a las comunidades tiempo para ir por delante y prepararse mejor. También constituye información crítica para el sector del transporte aéreo, ya que es posible anticipar demoras y las acciones que deben realizarse. El equipo del proyecto PERSEVERE, financiado con fondos europeos, estudia el uso de redes neuronales, concretamente de redes neuronales informadas por la física (PINN, por sus siglas en inglés) que combinan aprendizaje automático basado en datos y modelos físicos, para predecir el estado fluido de la atmósfera en regiones que afectan al tráfico aéreo. En PERSEVERE también se explorará el desarrollo de redes generativas antagónicas especializadas para mejorar y complementar el resultado de las PINN al aumentar la precisión de la predicción e incorporar variables adicionales como la temperatura y la humedad del aire.
Objetivo
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.
Ámbito científico
Palabras clave
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinador
28903 Getafe (Madrid)
España