Flowcean自动化生成可模块化使用的物理系统模型，提升建模效率。
Flowcean - Model Learning for Cyber-Physical Systems
- 基于数据驱动学习，支持多种算法与工具集成
- 提供模块化架构，适配多样化的建模任务
- 专为复杂物理系统设计，适合工程应用
有效的网络物理系统（CPS）模型对于其设计与运行至关重要。由于CPS固有的复杂性，构建此类模型既困难又耗时。因此，利用机器学习方法进行数据驱动的模型生成正日益流行。本文提出Flowcean，一种新颖的框架，旨在通过数据驱动学习实现模型的自动化生成，重点聚焦于模块化与可用性。该框架提供多种学习策略、数据处理方法和评估指标，为CPS场景提供全面解决方案。Flowcean通过模块化且灵活的架构，促进不同学习库与工具的集成，确保对广泛建模任务的适应性。这简化了模型生成与评估流程，使其更加高效且易于使用。
原文摘要 · Abstract (English)
Effective models of Cyber-Physical Systems (CPS) are crucial for their design and operation. Constructing such models is difficult and time-consuming due to the inherent complexity of CPS. As a result, data-driven model generation using machine learning methods is gaining popularity. In this paper, we present Flowcean, a novel framework designed to automate the generation of models through data-driven learning that focuses on modularity and usability. By offering various learning strategies, data processing methods, and evaluation metrics, our framework provides a comprehensive solution, tailored to CPS scenarios. Flowcean facilitates the integration of diverse learning libraries and tools within a modular and flexible architecture, ensuring adaptability to a wide range of modeling tasks. This streamlines the process of model generation and evaluation, making it more efficient and accessible.
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