Understand
Data-driven modeling of spatiotemporal physical processes with general deep learning methods is a highly challenging task.
- It is further exacerbated by the limited availability of data, leading to poor generalizations in standard neural network models.
- To tackle this issue, we introduce a new approach called the Finite Volume Neural Network (FINN).
- The FINN method adopts the numerical structure of the well-known Finite Volume Method for handling partial differential equations, so that each quantity of interest follows its own adaptable conservation law, while it concurrently accommodates learnable parameters.
Built on
A distributed neural network architecture for robust non-linear spatio-temporal prediction
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Object recognition with gradient-based learning , pp. 319–345
Yann LeCun, Patrick Haffner, Léon Bottou, and Yoshua Bengio · 1999
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Then
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Later among the works it cites.
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Later among the works it cites.
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