Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal.

IF 2.2 3区 物理与天体物理 Q2 PHYSICS, FLUIDS & PLASMAS
Mahindra Rautela, Alan Williams, Alexander Scheinker
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引用次数: 0

Abstract

Charged particle dynamics under the influence of electromagnetic fields is a challenging spatiotemporal problem. Many high-performance physics-based simulators for predicting behavior in a charged particle beam are computationally expensive, limiting their utility for solving inverse problems online. The problem of estimating upstream six-dimensional (6D) phase space given downstream measurements of charged particles in an accelerator is an inverse problem of growing importance. This paper introduces a reverse latent evolution model designed for the temporal inversion of forward beam dynamics. In this two-step self-supervised deep learning framework, we utilize a conditional variational autoencoder (CVAE) to project 6D phase space projections of a charged particle beam into a lower-dimensional latent distribution. Subsequently, we autoregressively learn the inverse temporal dynamics in the latent space using a long short-term memory (LSTM) network. The coupled CVAE-LSTM framework can predict 6D phase space projections across all upstream accelerating sections based on single or multiple downstream phase space measurements as inputs. The proposed model also captures the aleatoric uncertainty of the high-dimensional input data within the latent space. This uncertainty, which reflects potential uncertain measurements at a given module, is propagated through the LSTM network to estimate uncertainty bounds for all upstream predictions, demonstrating the robustness of the LSTM network to random perturbations in the input.

利用不确定性感知潜在演化反转的时空光束动力学时间反演。
电磁场作用下的带电粒子动力学是一个具有挑战性的时空问题。许多用于预测带电粒子束行为的高性能物理模拟器在计算上是昂贵的,这限制了它们在解决在线逆问题方面的效用。给定加速器中带电粒子的下游测量值,估计上游六维(6D)相空间的问题是一个日益重要的逆问题。本文介绍了一种用于前波束动力学时间反演的反向潜演化模型。在这个两步自监督深度学习框架中,我们利用条件变分自编码器(CVAE)将带电粒子束的6D相空间投影投影到低维潜在分布中。随后,我们使用长短期记忆(LSTM)网络自回归学习潜伏空间的逆时间动态。耦合CVAE-LSTM框架可以基于单个或多个下游相空间测量作为输入,预测所有上游加速段的6D相空间投影。该模型还捕获了潜在空间内高维输入数据的任意不确定性。这种不确定性反映了给定模块的潜在不确定性测量,通过LSTM网络传播,以估计所有上游预测的不确定性界限,证明了LSTM网络对输入中的随机扰动的鲁棒性。
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来源期刊
Physical Review E
Physical Review E PHYSICS, FLUIDS & PLASMASPHYSICS, MATHEMAT-PHYSICS, MATHEMATICAL
CiteScore
4.50
自引率
16.70%
发文量
2110
期刊介绍: Physical Review E (PRE), broad and interdisciplinary in scope, focuses on collective phenomena of many-body systems, with statistical physics and nonlinear dynamics as the central themes of the journal. Physical Review E publishes recent developments in biological and soft matter physics including granular materials, colloids, complex fluids, liquid crystals, and polymers. The journal covers fluid dynamics and plasma physics and includes sections on computational and interdisciplinary physics, for example, complex networks.
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