  Machine learning (ML) tools such as encoder-decoder deep convolutional neural
networks (CNN) are able to extract relationships between inputs and outputs of
large complex systems directly from raw data. For time-varying systems the
predictive capabilities of ML tools degrade as the systems are no longer
accurately represented by the data sets with which the ML models were trained.
Re-training is possible, but only if the changes are slow and if new
input-output training data measurements can be made online non-invasively. In
this work we present an approach to deep learning for time-varying systems in
which adaptive feedback based only on available system output measurements is
applied to encoded low-dimensional dense layers of encoder-decoder type CNNs.
We demonstrate our method in developing an inverse model of a complex charged
particle accelerator system, mapping output beam measurements to input beam
distributions while both the accelerator components and the unknown input beam
distribution quickly vary with time. We demonstrate our results using
experimental measurements of the input and output beam distributions of the
HiRES ultra-fast electron diffraction (UED) microscopy beam line at Lawrence
Berkeley National Laboratory. We show how our method can be used to aid both
physics and ML-based surrogate online models to provide non-invasive beam
diagnostics and we also demonstrate how our method can be used to automatically
track the time varying quantum efficiency map of a particle accelerator's
photocathode.
