Neural ODE and neural SDE models trained on experimental particle tracks reproduce long-time statistics of a chaotic settling sphere, with deterministic models generalizing better to new initial conditions.
A common approach in data-driven modeling is to approximate the time evolution of x using a learned function g as follows dx dt = g(x)
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Data-driven modeling of a settling sphere in a quiescent medium
Neural ODE and neural SDE models trained on experimental particle tracks reproduce long-time statistics of a chaotic settling sphere, with deterministic models generalizing better to new initial conditions.