  We generate $\sim$ 100,000 model spectra of Type Ia Supernovae (SNIa) to form
a spectral library for the purpose of building an Artificial Intelligence
Assisted Inversion (AIAI) algorithm for theoretical models. As a first attempt,
we restrict our studies to time around $B$-band maximum and compute theoretical
spectra with a broad spectral wavelength coverage from 2000 $-$ 10000 ${\rm
\AA}$ using the code TARDIS. Based on the library of theoretically calculated
spectra, we construct the AIAI algorithm with a Multi-Residual Convolutional
Neural Network (MRNN) to retrieve the contributions of different ionic species
to the heavily blended spectral profiles of the theoretical spectra. The AIAI
is found to be very powerful in distinguishing spectral patterns due to coupled
atomic transitions and has the capacity of quantitatively measuring the
contributions from different ionic species. By applying the AIAI algorithm to a
set of well observed SNIa spectra, we demonstrate that the model can yield
powerful constraints on the chemical structures of these SNIa. Using the
chemical structures deduced from AIAI, we successfully reconstructed the
observed data, thus confirming the validity of the method. We show that the
light curve decline rate of SNIa is correlated with the amount of $^{56}$Ni
above the photosphere in the ejecta. We detect a clear decrease of $^{56}$Ni
mass with time that can be attributed to its radioactive decay. Our Code and
model spectra are made available on the website
\href{https://github.com/GeronimoChen/AIAI-Supernova}{https://github.com/GeronimoChen/AIAI-Supernova}.
