PG-NODE reformulation of the SLIR TB model lets neural nets learn time-varying rates, yielding 27% lower RMSE than classical ODEs when correcting for unmodeled treatment and relapse effects in simulations.
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$PG-NODE^{TB}$: Physics-Guided Neural Ordinary Differential Equations for Tuberculosis Transmission Dynamics
PG-NODE reformulation of the SLIR TB model lets neural nets learn time-varying rates, yielding 27% lower RMSE than classical ODEs when correcting for unmodeled treatment and relapse effects in simulations.