Adaptive Deep Learning for Time-Varying Systems With Hidden Parameters: Predicting Changing Input Beam Distributions of Compact Particle Accelerators

Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems which does not require re-training. Our approach is to include adaptive feedback in the architecture of deep generative convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. Our approach is inspired by biological systems in which separate groups of neurons interact and are controlled and synchronized by external feedbacks. We demonstrate this approach by 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 vary rapidly with time. We demonstrate our methods on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics. We also demonstrate our method for automatically tracking the time varying quantum efficiency map of a particle accelerator’s photocathode.