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Found 2 papers, 1 papers with code
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.
Reducing Effects of Swath Gaps on Unsupervised Machine Learning Models for NASA MODIS Instruments
We compare the model trained with our augmentation techniques on the swath gap-filled data with the model trained on the original swath gap-less data and note highly augmented performance.
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