  A simulation is useful when the phenomenon of interest is either expensive to
regenerate or irreproducible with the same context. Recently, Bayesian
inference on the distribution of the simulation input parameter has been
implemented sequentially to minimize the required simulation budget for the
task of simulation validation to the real-world. However, the Bayesian
inference is still challenging when the ground-truth posterior is multi-modal
with a high-dimensional simulation output. This paper introduces a
regularization technique, namely Neural Posterior Regularization (NPR), which
enforces the model to explore the input parameter space effectively. Afterward,
we provide the closed-form solution of the regularized optimization that
enables analyzing the effect of the regularization. We empirically validate
that NPR attains the statistically significant gain on benchmark performances
for diverse simulation tasks.
