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Found 2 papers, 1 papers with code
Gradient-enhanced deep Gaussian processes for multifidelity modelling
Multifidelity models integrate data from multiple sources to produce a single approximator for the underlying process.
Neural network architectures using min-plus algebra for solving certain high dimensional optimal control problems and Hamilton-Jacobi PDEs
In this paper, we propose two abstract neural network architectures which are respectively used to compute the value function and the optimal control for certain class of high dimensional optimal control problems.
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