  As charged particle bunches become shorter and more intense, the effects of
nonlinear intra-bunch collective interactions such as space charge forces and
bunch-to-bunch influences such as wakefields and coherent synchrotron radiation
also increase. Shorter more intense bunches are also more difficult to
accurately image because their dimensions are beyond the resolution of existing
diagnostics and they may be destructive to intercepting diagnostics. The
limited availability of detailed diagnostics for intense high energy beams is a
fundamental challenge for the accelerator community because both beams and
accelerators are time-varying systems that change in unpredictable ways. The
detailed 6D (x,y,z,px,py,pz) distributions of beams emerging from sources vary
with time due to factors such as evolving photocathode laser intensity profiles
and the quantum efficiency of photocathodes. Accelerator magnets, RF
amplifiers, and control systems are perturbed by external disturbances,
beam-loading effects, temperature variations, and misalignments. Although
machine learning (ML) methods have grown in popularity in the accelerator
community in recently years, they are fundamentally limited when it comes to
time-varying systems for which most current approaches are to simply collect
large new data sets and perform re-training, something which is not feasible
for busy accelerator user facilities because detailed beam measurements usually
interrupt operations. New adaptive machine learning (AML) methods designed for
time-varying systems are needed to aid in the diagnostics and control of
high-intensity, ultrashort beams by combining deep learning tools such as
convolutional neural network-based encoder-decoder architectures,
model-independent feedback, physics constraints, and online models with real
time non-invasive beam data, to provide a detailed virtual view of intense
bunch dynamics.
