A differentiable Kalman filter that learns the state-transition operator by field inversion and a neural closure model is demonstrated on rocket and Allen-Cahn models, with reported 90% error reductions over a fixed-model Kalman filter.
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DKFNet: Differentiable Kalman Filter for Field Inversion and Machine Learning
A differentiable Kalman filter that learns the state-transition operator by field inversion and a neural closure model is demonstrated on rocket and Allen-Cahn models, with reported 90% error reductions over a fixed-model Kalman filter.