  Machine learning (ML) tools such as encoder-decoder convolutional neural
networks (CNN) can represent incredibly complex nonlinear functions which map
between combinations of images and scalars. For example, CNNs can be used to
map combinations of accelerator parameters and images which are 2D projections
of the 6D phase space distributions of charged particle beams as they are
transported between various particle accelerator locations. Despite their
strengths, applying ML to time-varying systems, or systems with shifting
distributions, is an open problem, especially for large systems for which
collecting new data for re-training is impractical or interrupts operations.
Particle accelerators are one example of large time-varying systems for which
collecting detailed training data requires lengthy dedicated beam measurements
which may no longer be available during regular operations. We present a
recently developed method of adaptive ML for time-varying systems. Our approach
is to map very high (N>100k) dimensional inputs (a combination of scalar
parameters and images) into the low dimensional (N~2) latent space at the
output of the encoder section of an encoder-decoder CNN. We then actively tune
the low dimensional latent space-based representation of complex system
dynamics by the addition of an adaptively tuned feedback vector directly before
the decoder sections builds back up to our image-based high-dimensional phase
space density representations. This method allows us to learn correlations
within and to quickly tune the characteristics of incredibly high parameter
systems and to track their evolution in real time based on feedback without
massive new data sets for re-training.
