PERSEVERE · Physics-informed nEuRal networks for SEVERe wEather event prediction

Physics-informed nEuRal networks for SEVERe wEather event prediction

Currently, there is constant word about how artificial intelligence is affecting our daily lives and how its progress may have a significant impact in the way we approach our daily tasks and challenges. Neural networks, a particular kind of artificial intelligence resembling the human brain, are entering our work environments with strength and many useful perspectives for the future. The novel development of physics-informed neural networks (PINNs), which incorporate the constraints given by physics laws into the training process, has open the gates to numerous applications, e.g. the reconstruction of fluid flows. PINNs may regularize fluid information by means of applying the Navier-Stokes equations as a relevant contribution to the loss function during the training of such networks, recovering / regularizing / reconstructing the fluid domain in areas where experiments are limited by technology. On the other hand, the development of variational autoencoders (VAEs), which aim to learn compact probabilistic representations of complex data, offers an efficient and low-cost alternative for generative modeling in high-dimensional systems. VAEs are particularly suited for capturing the intrinsic variability of fluid flow fields and reconstructing incomplete or noisy measurements in spatiotemporal systems such as atmospheric dynamics. In weather forecasting applications, where high-resolution measurements of wind, humidity, or temperature may be unavailable or partially corrupted, VAEs can learn a latent representation of historical meteorological patterns and reconstruct plausible realizations of missing data. When combined with PINNs, which enforce physical consistency in the latent space by embedding governing equations like Navier-Stokes, the resulting hybrid models can both respect the physical laws and generalize from learned data distributions. This synergy proves especially powerful in scenarios involving the prediction of severe weather conditions, where rapid and accurate reconstructions of atmospheric flow fields are needed, for example, around airports, where storms have a severe impact on aircraft performance and safety. Following that trend, 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 forecast of severe weather events is therefore of crucial importance. We aim to develop an artificial intelligence based on neural networks which combines the strengths of PINNs and VAEs, so that one may rapidly estimate the fluidic behaviour of a moving storm and be able to take preventive measurements.

Data: CORDIS, © European Union