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Found 6 papers, 2 papers with code
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network
To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source.
Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models
To quickly assess the spatiotemporal variations of groundwater contamination under uncertain climate disturbances, we developed a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Operator (U-FNO) to solve Partial Differential Equations (PDEs) of groundwater flow and transport simulations at the site scale. We develop a combined loss function that includes both data-driven factors and physical boundary constraints at multiple spatiotemporal scales.
Insight into cloud processes from unsupervised classification with a rotationally invariant autoencoder
Clouds play a critical role in the Earth's energy budget and their potential changes are one of the largest uncertainties in future climate projections.
Cloud Classification with Unsupervised Deep Learning
We present a framework for cloud characterization that leverages modern unsupervised deep learning technologies.
AICCA: AI-driven Cloud Classification Atlas
Clouds play an important role in the Earth's energy budget and their behavior is one of the largest uncertainties in future climate projections.
Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder
Advanced satellite-born remote sensing instruments produce high-resolution multi-spectral data for much of the globe at a daily cadence.
