  Precise scientific analysis in collider-based particle physics is possible
because of complex simulations that connect fundamental theories to observable
quantities. The significant computational cost of these programs limits the
scope, precision, and accuracy of Standard Model measurements and searches for
new phenomena. We therefore introduce Deep neural networks using Classification
for Tuning and Reweighting (DCTR), a neural network-based approach to reweight
and fit simulations using all kinematic and flavor information -- the full
phase space. DCTR can perform tasks that are currently not possible with
existing methods, such as estimating non-perturbative fragmentation
uncertainties. The core idea behind the new approach is to exploit powerful
high-dimensional classifiers to reweight phase space as well as to identify the
best parameters for describing data. Numerical examples from
$e^+e^-\rightarrow\text{jets}$ demonstrate the fidelity of these methods for
simulation parameters that have a big and broad impact on phase space as well
as those that have a minimal and/or localized impact. The high fidelity of the
full phase-space reweighting enables a new paradigm for simulations, parameter
tuning, and model systematic uncertainties across particle physics and possibly
beyond.
