Estimating the probability of failure for complex real-world systems using high-fidelity computational models is often prohibitively expensive, especially when the probability is small. Exploiting low-fidelity models can make this process more feasible, but merging information from multiple low-fidelity and high-fidelity models poses several challenges. This paper presents a robust multi-fidelity surrogate modeling strategy in which the multi-fidelity surrogate is assembled using an active learning strategy using an on-the-fly model adequacy assessment set within a subset simulation framework for efficient reliability analysis. The multi-fidelity surrogate is assembled by first applying a Gaussian process correction to each low-fidelity model and assigning a model probability based on the model's local predictive accuracy and cost. Three strategies are proposed to fuse these individual surrogates into an overall surrogate model based on model averaging and deterministic/stochastic model selection. The strategies also dictate which model evaluations are necessary. No assumptions are made about the relationships between low-fidelity models, while the high-fidelity model is assumed to be the most accurate and most computationally expensive model. Through two analytical and two numerical case studies, including a case study evaluating the failure probability of Tristructural isotropic-coated (TRISO) nuclear fuels, the algorithm is shown to be highly accurate while drastically reducing the number of high-fidelity model calls (and hence computational cost).

TRISTRUCCUCTIONATIOPIC（TRISO）涂层颗粒燃料是强大的核燃料，并确定其可靠性对于先进的核技术的成功至关重要。然而，Triso失效概率很小，相关的计算模型很昂贵。我们使用耦合的主动学习，多尺度建模和子集模拟来估计使用几个1D和2D模型的Triso燃料的故障概率。通过多尺度建模，我们用来自两个低保真（LF）模型的信息融合，取代了昂贵的高保真（HF）模型评估。对于1D TRISO模型，我们考虑了三种多倍性建模策略：仅克里格，Kriging LF预测加克里格校正，深神经网络（DNN）LF预测加克里格校正。虽然这些多尺度建模策略的结果令人满意地比较了从两个LF模型中使用信息融合的策略，但是通常常常称为HF模型。接下来，对于2D Triso模型，我们考虑了两个多倍性建模策略：DNN LF预测加克里格校正（数据驱动）和1D Triso LF预测加克里格校正（基于物理学）。正如所预期的那样，基于物理的策略一直需要对HF模型的最少的呼叫。然而，由于DNN预测是瞬时的，数据驱动的策略具有较低的整体模拟时间，并且1D Triso模型需要不可忽略的模拟时间。

Skeleton-based Motion Capture (MoCap) systems have been widely used in the game and film industry for mimicking complex human actions for a long time. MoCap data has also proved its effectiveness in human activity recognition tasks. However, it is a quite challenging task for smaller datasets. The lack of such data for industrial activities further adds to the difficulties. In this work, we have proposed an ensemble-based machine learning methodology that is targeted to work better on MoCap datasets. The experiments have been performed on the MoCap data given in the Bento Packaging Activity Recognition Challenge 2021. Bento is a Japanese word that resembles lunch-box. Upon processing the raw MoCap data at first, we have achieved an astonishing accuracy of 98% on 10-fold Cross-Validation and 82% on Leave-One-Out-Cross-Validation by using the proposed ensemble model.