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Found 3 papers, 1 papers with code
Data-driven discovery of PDEs in complex datasets
A different approach is to measure the quantities of interest and use deep learning to reverse engineer the PDEs which are describing the physical process.
Neural network augmented inverse problems for PDEs
In this paper we show how to augment classical methods for inverse problems with artificial neural networks.
A unified deep artificial neural network approach to partial differential equations in complex geometries
In this paper we use deep feedforward artificial neural networks to approximate solutions to partial differential equations in complex geometries.
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