  Transiting exoplanets in multi-planet systems exhibit non-Keplerian orbits as
a result of the gravitational influence from companions which can cause the
times and durations of transits to vary. The amplitude and periodicity of the
transit time variations (TTV) are characteristic of the perturbing planet's
mass and orbit. The objects of interest (TOI) from the Transiting Exoplanet
Survey Satellite (TESS) are analyzed in a uniform way to search for TTVs with
sectors 1-3 of data. Due to the volume of targets in the TESS candidate list,
artificial intelligence is used to expedite the search for planets by vetting
non-transit signals prior to characterizing the light curve time series. The
residuals of fitting a linear orbit ephemeris are used to search for transit
timing variations. The significance of a perturbing planet is assessed by
comparing the Bayesian evidence between a linear and non-linear ephemeris,
which is based on an N-body simulation. Nested sampling is used to derive
posterior distributions for the N-body ephemeris and in order to expedite
convergence custom priors are designed using machine learning. A dual input,
multi-output convolutional neural network is designed to predict the parameters
of a perturbing body given the known parameters and measured perturbation
(O-C). There is evidence for 3 new multi-planet candidates (WASP-18, WASP-126,
TOI-193) with non-transiting companions using the 2 minute cadence observations
from TESS. This approach can be used to identify multi-planet systems and stars
in need of longer radial velocity and photometric follow-up than those already
performed.
