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Found 3 papers, 1 papers with code
Efficient and Flexible Inference for Stochastic Systems
Many real world dynamical systems are described by stochastic differential equations.
Scalable Variational Inference for Dynamical Systems
That is why, despite the high computational cost, numerical integration is still the gold standard in many applications.
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes
The essence of gradient matching is to model the prior over state variables as a Gaussian process which implies that the joint distribution given the ODE's and GP kernels is also Gaussian distributed.
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