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physics.flu-dyn2023
Super-resolving sparse observations in partial differential equations: A physics-constrained convolutional neural network approach
Daniel Kelshaw, Luca Magri
We propose the physics-constrained convolutional neural network (PC-CNN) to infer the high-resolution solution from sparse observations of spatiotemporal and nonlinear partial diff…
physics.flu-dyn2023★ 3 cited
Uncovering solutions from data corrupted by systematic errors: A physics-constrained convolutional neural network approach
Daniel Kelshaw, Luca Magri
Information on natural phenomena and engineering systems is typically contained in data. Data can be corrupted by systematic errors in models and experiments. In this paper, we pro…