A two-phase training scheme that adds tangent-linear and adjoint loss terms to a neural network emulator of Lorenz 96 improves Jacobian consistency while preserving forecast accuracy.
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Jacobian-Enforced Neural Networks (JENN) for Improved Data Assimilation Consistency in Dynamical Models
A two-phase training scheme that adds tangent-linear and adjoint loss terms to a neural network emulator of Lorenz 96 improves Jacobian consistency while preserving forecast accuracy.