LIBTwinSVM: A Library for Twin Support Vector Machines

From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning

This work develops a methodology for creating a data-driven digital twin from a library of physics-based models representing various asset states. The digital twin is updated using interpretable machine learning. Specifically, we use optimal trees---a recently developed scalable machine learning method---to train an interpretable data-driven classifier. Training data for the classifier are generated offline using simulated scenarios solved by the library of physics-based models. These data can be further augmented using experimental or other historical data. In operation, the classifier uses o…

Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems

A framework for creating and updating digital twins for dynamical systems from a library of physics-based functions is proposed. The sparse Bayesian machine learning is used to update and derive an interpretable expression for the digital twin. Two approaches for updating the digital twin are proposed. The first approach makes use of both the input and output information from a dynamical system, whereas the second approach utilizes output-only observations to update the digital twin. Both methods use a library of candidate functions representing certain physics to infer new perturbation terms …

使用机器学习和降阶模型的热管理预测性数字孪生

Predictive Digital Twins for Thermal Management Using Machine Learning and Reduced-Order Models

针对汽车前照灯散热器热管理，本文融合CFD降阶模型与监督学习构建预测性数字孪生，由神经网络预测最优ROM配置，MAE为54.240且精度最佳。

SupplyNetPy：用于任意供应链和库存网络高保真建模与仿真的开源Python库

SupplyNetPy: An Open-Source Python Library for High-Fidelity Modeling and Simulation of Arbitrary Supply Chain and Inventory Networks

介绍开源Python库SupplyNetPy，用于任意供应链网络建模与离散事件仿真，支持多种策略和情况，组件可扩展。用户以图描述供应链，库处理仿真并提供报告，经详细验证能实现复杂模型编程生成与仿真等多种功能。

DesCartes Builder：一种开发基于机器学习的数字孪生工具

DesCartes Builder: A Tool to Develop Machine-Learning Based Digital Twins

针对数字孪生（DT）工程中机器学习（ML）使用缺乏结构化方法的问题，本文提出开源工具DesCartes Builder，通过可视化数据流范式及定制化算法库，支持基于ML的实时DT原型与实例的系统性开发，并在土木工程用例中验证其有效性。