  In this paper, we discuss the way advanced machine learning techniques allow
physicists to perform in-depth studies of the realistic operating modes of the
detectors during the stage of their design. Proposed approach can be applied to
both design concept (CDR) and technical design (TDR) phases of future detectors
and existing detectors if upgraded. The machine learning approaches may speed
up the verification of the possible detector configurations and will automate
the entire detector R\&D, which is often accompanied by a large number of
scattered studies. We present the approach of using machine learning for
detector R\&D and its optimisation cycle with an emphasis on the project of the
electromagnetic calorimeter upgrade for the LHCb detector\cite{lhcls3}. The
spatial reconstruction and time of arrival properties for the electromagnetic
calorimeter were demonstrated.
