Using neural flow-field predictions as initial guesses for Newton-Krylov iterations turns a fast but unreliable surrogate into a solver-accepted steady CFD solution, cutting residual error by orders of magnitude.
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Reliable and efficient steady CFD from surrogate predictions through Newton-Krylov correction
Using neural flow-field predictions as initial guesses for Newton-Krylov iterations turns a fast but unreliable surrogate into a solver-accepted steady CFD solution, cutting residual error by orders of magnitude.