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Following our previous study of Artificial Intelligence Assisted Inversion(AIAI) of supernova analyses (Chen et al. 2020), we train a set of deep neuralnetworks based on the one-dimensional radiative transfer code TARDIS(Kerzendorf & Sim 2014) to simulate the optical spectra of Type Ia supernovae(SNe Ia) between 10 and 40 days after the explosion. The neural networks areapplied to derive the mass of 56Ni in velocity ranges well above thephotosphere for a sample of 153 well-observed SNe Ia. Many SNe have multi-epochobservations for which the decay of the radioactive 56Ni can be testedquantitatively. The 56Ni mass derived from AIAI using the observed spectra asinput for the sample is found to agree with the theoretical 56Ni decay rate.The AIAI reveals a spectral signature near 3890 \AA which can be identified asbeing produced by multiple Ni II lines between 3950 and 4100 \AA. The massdeduced from AIAI is correlated to the light-curve shapes of SNe Ia, with theSNe Ia with broader light curves showing larger 56Ni mass in the envelope. AIAIenables spectral data of SNe to be quantitatively analyzed under theoreticalframeworks based on well-defined physical assumptions.

Artificial Intelligence Assisted Inversion (AIAI): Quantifying the Spectral Features of $^{56}$Ni of Type Ia Supernovae