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Found 2 papers, 1 papers with code
Learning continuous models for continuous physics
A core issue with this approach is that ML models are typically trained on discrete data, using ML methodologies that are not aware of underlying continuity properties, which results in models that often do not capture the underlying continuous dynamics of a system of interest.
Continuous-in-Depth Neural Networks
We first show that ResNets fail to be meaningful dynamical integrators in this richer sense.
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