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Physics-informed computer vision: A review and perspectives
The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …
transforming many application domains. Here the learning process is augmented through …
Foundation models for weather and climate data understanding: A comprehensive survey
As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …
sciences is increasingly adopting data-driven models, powered by progressive …
Skilful precipitation nowcasting using deep generative models of radar
Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours
ahead, supports the real-world socioeconomic needs of many sectors reliant on weather …
ahead, supports the real-world socioeconomic needs of many sectors reliant on weather …
Physics Informed Machine Learning (PIML) for design, management and resilience-development of urban infrastructures: A review
Building resilient and sustainable urban infrastructures is imperative to prepare future
generations against new pandemics and climate change uncertainties. In general …
generations against new pandemics and climate change uncertainties. In general …
[HTML][HTML] Domain knowledge-driven variational recurrent networks for drought monitoring
In the context of climate change, droughts, increasingly frequent and severe, necessitate
effective monitoring. Existing methods, such as drought indices and data-driven models …
effective monitoring. Existing methods, such as drought indices and data-driven models …
[PDF][PDF] GAN for time series prediction, data assimilation and uncertainty quantification
We propose a new method in which a generative adversarial network (GAN) is used to
quantify the uncertainty of forward simulations in the presence of observed data. Previously …
quantify the uncertainty of forward simulations in the presence of observed data. Previously …
Using variants of conditional-decoder VAE for spatial-temporal precipitation nowcasting in Thailand
This paper proposes variants of conditional-decoder variational autoencoder based on
convolutional gated recurrent unit (ConvGRU), namely CD-VAEs for spatial-temporal …
convolutional gated recurrent unit (ConvGRU), namely CD-VAEs for spatial-temporal …
[PDF][PDF] Hybrid Recurrent Neural Network for Drought Monitoring
Droughts are pervasive hydrometeorological phenomena and global hazards, whose
frequency and intensity are expected to increase in the context of climate change. Drought …
frequency and intensity are expected to increase in the context of climate change. Drought …