Expanding LUME to Support Virtual Accelerators and Digital Twins
扩展 LUME 以支持虚拟加速器和数字孪生
Ryan Roussel, Christopher M. Pierce, Sara Miskovich, Gopika Bhardwaj, Jeremy Lorelli, Ken Lauer, Auralee Edelen, Christopher Mayes
AI总结 本文扩展 LUME Python 包,通过引入 LUMEModel 抽象和变量系统,实现跨异构仿真后端和控制系统的虚拟加速器与数字孪生的标准化部署,提升可重用性和灵活性。
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虚拟加速器和数字孪生正日益成为加速器运行、控制开发与验证以及基于模型优化的关键工具。然而,当前的实现通常与特定的仿真代码、设施和应用紧密耦合,导致碎片化、临时性的解决方案难以重用或扩展。为解决这一问题,我们扩展了 LUME Python 包,使其能够跨异构仿真后端和控制系统接口实现虚拟加速器和数字孪生的标准化部署与实现。这一变化的核心是引入了 LUMEModel 抽象,它定义了一个固定的、与模拟器无关的 API 和一个变量系统,用于编码元数据,如单位、数据类型/验证。该设计支持与基于物理的模拟器、代理模型和可微分仿真的标准化交互,同时通过 lume-pva 包支持 Python 原生工作流和基于 EPICS 的 IOC 操作。设施和模拟器特定的细节通过可扩展的转换器层封装,从而将一致的控制系统语义映射到不同的仿真引擎上。我们描述了 LUMEModel 架构、变量系统和包生态系统,并展示了代表性用例,包括模型互换性、分阶段和链式模拟器以及持续集成测试。这项工作将使虚拟加速器的实现和使用更加容易和灵活。
Virtual accelerators and digital twins are increasingly essential tools for accelerator operations, controls development and verification, and model-based optimization. However, current implementations are often tightly coupled to specific simulation codes, facilities, and applications, resulting in fragmented, ad hoc solutions that are difficult to reuse or extend. To address this, we expand the LUME Python package to include standardized implementation and deployment of virtual accelerators and digital twins across heterogeneous simulation backends and control system interfaces. At the core of this change is the introduction of LUMEModel abstraction, which defines a fixed, simulator-agnostic API and a variable system that encodes metadata such as units and data types/validation. This design enables standardized interaction with physics-based simulators, surrogate models, and differentiable simulations, while supporting both Python-native workflows and IOC-based operation via EPICS using the lume-pva package. Facility- and simulator-specific details are encapsulated through extensible transformer layers, allowing consistent control-system semantics to be mapped onto diverse simulation engines. We describe the LUMEModel architecture, variable system, and package ecosystem, and present representative use cases including model interchangeability, staged and chained simulators, and continuous integration testing. This work will make implementing and using virtual accelerators easier and more flexible.