Autonomous Cooking with Digital Twin Methodology

Endeavouring Intelligent Process Self-Control by Employing Digital Twin Methodology: Proof-of-Concept Study for Cooking Applications

This work demonstrates the use of the Digital Twin methodology to predict the water concentration and temperature of chicken meat. It marks a milestone on the path to autonomous cooking devices that do not only control device temperatures but culinary food quality markers as well. A custom water transport equation is coupled to the energy equation. The transport equations are implemented in ANSYS Fluent 2019R2 via User Defined Function (UDF) code written in C. The model is in good agreement with experiments provided by project partners. Thermal fluid-structure interaction simulations of pan-fr…

Continuous calibration of a digital twin: comparison of particle filter and Bayesian calibration approaches

Assimilation of continuously streamed monitored data is an essential component of a digital twin; the assimilated data are used to ensure the digital twin is a true representation of the monitored system. One way this is achieved is by calibration of simulation models, whether data-derived or physics-based, or a combination of both. Traditional manual calibration is not possible in this context hence new methods are required for continuous calibration. In this paper, a particle filter methodology for continuous calibration of the physics-based model element of a digital twin is presented and a…

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…

桥梁数字孪生概率校准中嵌入模型形式不确定性

Embedding Model-form Uncertainty in Probabilistic Calibration of Digital Twins for Bridges

本研究提出一种通过随机参数嵌入显式量化模型形式不确定性的桥梁数字孪生校准框架，采用三步策略和方差分解，在Nibelungenbrücke桥梁上验证，提升了预测不确定性的可解释性与可靠性。

具有量化不确定性的预测性数字孪生，用于肿瘤学中患者特异性决策

Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology

本文提出端到端数据到决策的肿瘤数字孪生方法，融合纵向影像与反应-扩散机制模型，利用贝叶斯后验近似量化不确定性，并经虚拟患者和临床队列验证。

数字孪生的因果证伪

Causal Falsification of Digital Twins

本研究针对安全关键场景下数字孪生的准确性评估问题，提出仅需独立同分布观测轨迹数据的通用统计因果证伪方法，将其应用于Pulse生理引擎脓毒症建模，采用MIMIC-III数据集验证，可应对数据混杂问题。