  Consistent Recalibration models (CRC) have been introduced to capture in
necessary generality the dynamic features of term structures of derivatives'
prices. Several approaches have been suggested to tackle this problem, but all
of them, including CRC models, suffered from numerical intractabilities mainly
due to the presence of complicated drift terms or consistency conditions. We
overcome this problem by machine learning techniques, which allow to store the
crucial drift term's information in neural network type functions. This yields
first time dynamic term structure models which can be efficiently simulated.
