Operator Neural Jump ODEs provably converge, in the training loss and in the observation metric d_k, to the conditional expectation of L2(Ξ)-valued stochastic processes observed at random times and spatial points.
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Operator Neural Jump ODEs: $L^2$-optimal prediction in function spaces
Operator Neural Jump ODEs provably converge, in the training loss and in the observation metric d_k, to the conditional expectation of L2(Ξ)-valued stochastic processes observed at random times and spatial points.