Abstract
Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches.
Highlights
Valentino and Giorgio KaniadakisA simple code for the adaptive control algorithm used here can be downloaded from: https://github.com/alexscheinker/ES_adaptive_optimization, accessed on 8 April 2021.Machine learning (ML) [1] tools such as neural networks (NN) [2], Gaussian processes (GP) [3], and reinforcement learning (RL) in which NNs are incorporated to represent system models and optimal feedbacks [4], have been growing in popularity for particle accelerator applications
These methods have been around for decades, their recent growth in popularity can be attributed to recent growth in computing power with high performance computers and especially graphics processing units (GPUs) becoming very inexpensive
Recent ML applications for accelerators include ML-enhanced genetic optimization [5], utilizing surrogate models for simulation-based optimization studies and for estimating beam characteristics [6,7,8,9,10], Bayesian and GP approaches for accelerator tuning [11,12,13,14,15,16], various applications at the Large Hardon Collider including optics corrections and detecting faulty beam position monitors [17,18,19], powerful polynomial chaos expansion-based surrogate models for uncertainty quantification have been developed [20], and RL tools have been developed for online accelerator optimization [21,22,23,24,25]
Summary
A simple code for the adaptive control algorithm used here can be downloaded from: https://github.com/alexscheinker/ES_adaptive_optimization, accessed on 8 April 2021. Novel adaptive feedback algorithms have been developed which are able to tune large groups of parameters simultaneously based only on noisy scalar measurements with analytic proofs of convergence and analytically known guarantees on parameter update rates, which makes them especially well suited for particle accelerator problems [26] Such methods can be implemented via custom python scripts that read and write from machine components via network systems such as EPICS [27] and have been implemented in powerful optimization software such as OCELOT for online accelerator tuning [28]. We demonstrate that by using only available non-invasive measurements of the transverse root mean square X and Y beam sizes at the end of this accelerator section, we can adaptively feedback and tune the predictions of a trained neural network to accurately predict the beam envelope evolution throughout the accelerator section, thereby serving as an online, non-invasive, adaptive virtual beam diagnostic
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