Adaptive Latent Space Tuning for Non-Stationary Distributions

TL;DR

This paper introduces an adaptive method for tuning the latent space of deep CNNs to handle rapidly changing, non-stationary systems in real-time, demonstrated on particle beam prediction.

Contribution

It presents a novel real-time adaptive tuning approach for encoder-decoder CNNs to manage fast distribution shifts without re-training.

Findings

Effective in predicting time-varying particle beam properties.

Handles unknown, rapid distribution changes successfully.

Reduces need for frequent re-training in dynamic systems.

Abstract

Powerful deep learning tools, such as convolutional neural networks (CNN), are able to learn the input-output relationships of large complicated systems directly from data. Encoder-decoder deep CNNs are able to extract features directly from images, mix them with scalar inputs within a general low-dimensional latent space, and then generate new complex 2D outputs which represent complex physical phenomenon. One important challenge faced by deep learning methods is large non-stationary systems whose characteristics change quickly with time for which re-training is not feasible. In this paper we present a method for adaptive tuning of the low-dimensional latent space of deep encoder-decoder style CNNs based on real-time feedback to quickly compensate for unknown and fast distribution shifts. We demonstrate our approach for predicting the properties of a time-varying charged particle beam in a particle accelerator whose components (accelerating electric fields and focusing magnetic fields) are also quickly changing with time.