Assumed density filtering and smoothing for neural network surrogate models is enabled by analytic computation of output moments, yielding more accurate state estimates and improved LQR performance on stochastic Lorenz and Wiener systems.
15 KUANGLIN Supplementary material Contents 1 Introduction 1 2 Notation 2 3 Problem statement 3 4 Neural networks 4 4.1 The identity-augmentation operator
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Assumed Density Filtering and Smoothing with Neural Network Surrogate Models
Assumed density filtering and smoothing for neural network surrogate models is enabled by analytic computation of output moments, yielding more accurate state estimates and improved LQR performance on stochastic Lorenz and Wiener systems.