Print Email Facebook Twitter Physics-informed machine learning for nowcasting extreme rainfall Title Physics-informed machine learning for nowcasting extreme rainfall Author Yin, Junzhe (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Dauwels, J.H.G. (mentor) Abelmann, L. (graduation committee) Uijlenhoet, R. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Circuits and Systems Date 2024-03-25 Abstract The thesis explores an innovative technique for enhancing the precision of short-term weather forecasts, particularly in predicting extreme weather phenomena, which present a notable challenge for existing models such as PySTEPS due to their volatile behavior. Leveraging precipitation and meteorological data sourced from the Royal Netherlands Meteorological Institute (KNMI), the research innovates through the development of a physics-informed neural network. Central to this approach is the implementation of a Physics-Informed Discriminator GAN (PID-GAN), a method that embeds physical principles directly into the adversarial training regime. The architecture is marked by the integration of a Vector Quantization Generative Adversarial Network (VQ-GAN) and a Transformer as the generator, complemented by a temporal discriminator as the discriminator component. Results from this study indicate a notable advancement over traditional numerical weather prediction and cutting-edge deep learning models, underscoring the PID-GAN model's superiority in delivering accurate precipitation nowcasting metrics. Subject Deep LearningNowcastingPhysics informed machine learning To reference this document use: http://resolver.tudelft.nl/uuid:4fe7cd89-5f7c-468b-8f8b-d2c493be9386 Part of collection Student theses Document type master thesis Rights © 2024 Junzhe Yin Files PDF thesis.pdf 13.72 MB Close viewer /islandora/object/uuid:4fe7cd89-5f7c-468b-8f8b-d2c493be9386/datastream/OBJ/view