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This paper addresses variational data assimilation from a learning point of view.
Deterministic Nonperiodic Flow
E.N. Lorenz · 1963
Earlier work this paper cites.
A family of embedded Runge-Kutta formulae
J. R. Dormand and P. J. Prince · 1980
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The computation of compressible and incompressible recirculating flows by a non-iterative implicit scheme
Raad I Issa, AD Gosman, and AP Watkins · 1986
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