  Detailed radiative transfer simulations of kilonovae are difficult to apply
directly to observations; they only sparsely cover simulation parameters, such
as the mass, velocity, morphology, and composition of the ejecta. On the other
hand, semianalytic models for kilonovae can be evaluated continuously over
model parameters, but neglect important physical details which are not
incorporated in the simulations, thus introducing systematic bias. Starting
with a grid of 2D anisotropic simulations of kilonova light curves covering a
wide range of ejecta properties, we apply adaptive-learning techniques to
iteratively choose new simulations and produce high-fidelity surrogate models
for those simulations. These surrogate models allow for continuous evaluation
across model parameters while retaining the microphysical details about the
ejecta. Using a new code for multimessenger inference, we demonstrate how to
use our interpolated models to infer kilonova parameters. Comparing to
inferences using simplified analytic models, we recover different ejecta
properties. We discuss the implications of this analysis which is qualitatively
consistent with similar previous work using detailed ejecta opacity
calculations and which illustrates systematic challenges for kilonova modeling.
An associated data and code release provides our interpolated light-curve
models, interpolation implementation which can be applied to reproduce our work
or extend to new models, and our multimessenger parameter inference engine.
