Data analyses in particle physics rely on an accu-
rate simulation of particle collisions and a detailed simula-
tion of detector effects to extract physics knowledge from
the recorded data. Event generators together with a geant-
based simulation of the detectors are used to produce large
samples of simulated events for analysis by the LHC experi-
ments. These simulations come at a high computational cost,
where the detector simulation and reconstruction algorithms
have the largest CPU demands. This article describes how
machine-learning (ML) techniques are used to reweight sim-
ulated samples obtained with a given set of parameters to
samples with different parameters or samples obtained from
entirely different simulation programs. The ML reweighting
method avoids the need for simulating the detector response
multiple times by incorporating the relevant information in a
single sample through event weights. Results are presented
for reweighting to model variations and higher-order calcula-
tions in simulated top quark pair production at the LHC. This
ML-based reweighting is an important element of the future
computing model of the CMS experiment and will facilitate
precision measurements at the High-Luminosity LHC.