A Julia chromatography solver combining high-order discretization with forward-mode automatic differentiation computes parameter gradients at about 1.4 forward solves per parameter, and finds FD-SBP faster than DG-SEM at matched accuracy.
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ChromOps.jl: High-order simulation and discrete forward sensitivity analysis for chromatography models
A Julia chromatography solver combining high-order discretization with forward-mode automatic differentiation computes parameter gradients at about 1.4 forward solves per parameter, and finds FD-SBP faster than DG-SEM at matched accuracy.