Modeling the effects of uncertainty is of crucial importance in the signal integrity and Electromagnetic Compatibility assessment of electronic products. In this article, a novel machine-learning-based approach for uncertainty quantification problems involving both random and epistemic variables is presented. The proposed methodology leverages evidence theory to represent probabilistic and epistemic uncertainties in a common framework. Then, Bayesian optimization is used to efficiently propagate this hybrid uncertainty on the performance of the system under study. Two suitable application examples validate the accuracy and efficiency of the proposed method.

中文翻译：

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用于 EMC 和 SI 评估的基于机器学习的混合随机模糊不确定性量化


对不确定性的影响进行建模对于电子产品的信号完整性和电磁兼容性评估至关重要。在本文中，提出了一种新颖的基于机器学习的方法，用于解决涉及随机变量和认知变量的不确定性量化问题。所提出的方法利用证据理论来表示通用框架中的概率和认知不确定性。然后，使用贝叶斯优化来有效地传播这种混合不确定性对所研究系统性能的影响。两个合适的应用实例验证了该方法的准确性和效率。