Data-driven models such as neural networks are being applied more and more to safety-critical applications, such as the modeling and control of cyber-physical systems. Despite the flexibility of the approach, there are still concerns about the safety of these models in this context, as well as the need for large amounts of potentially expensive data. In particular, when long-term predictions are needed or frequent measurements are not available, the open-loop stability of the model becomes important. However, it is difficult to make such guarantees for complex black-box models such as neural networks, and prior work has shown that model stability is indeed an issue. In this work, we consider an aluminum extraction process where measurements of the internal state of the reactor are time-consuming and expensive. We model the process using neural networks and investigate the role of including skip connections in the network architecture as well as using l1 regularization to induce sparse connection weights. We demonstrate that these measures can greatly improve both the accuracy and the stability of the models for datasets of varying sizes.

A digital twin is defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision-making. Unfortunately, the term remains vague and says little about its capability. Recently, the concept of capability level has been introduced to address this issue. Based on its capability, the concept states that a digital twin can be categorized on a scale from zero to five, referred to as standalone, descriptive, diagnostic, predictive, prescriptive, and autonomous, respectively. The current work introduces the concept in the context of the built environment. It demonstrates the concept by using a modern house as a use case. The house is equipped with an array of sensors that collect timeseries data regarding the internal state of the house. Together with physics-based and data-driven models, these data are used to develop digital twins at different capability levels demonstrated in virtual reality. The work, in addition to presenting a blueprint for developing digital twins, also provided future research directions to enhance the technology.

随着数据的不断增加，将现代机器学习方法应用于建模和控制等领域的兴趣爆炸。但是，尽管这种黑盒模型具有灵活性和令人惊讶的准确性，但仍然很难信任它们。结合两种方法的最新努力旨在开发灵活的模型，这些模型仍然可以很好地推广。我们称为混合分析和建模（HAM）的范式。在这项工作中，我们调查了使用数据驱动模型纠正基于错误的物理模型的纠正源术语方法（COSTA）。这使我们能够开发出可以进行准确预测的模型，即使问题的基本物理学尚未得到充分理解。我们将Costa应用于铝电解电池中的Hall-H \'Eroult工艺。我们证明该方法提高了准确性和预测稳定性，从而产生了总体可信赖的模型。

人工神经网络今天具有广泛的应用程序，因为它们的高度灵活性和从数据中建模非线性功能的能力。但是，由于其黑盒性质，从小型数据集概括的能力差以及在培训期间的不一致的融合，神经网络的可信度受到限制。铝电解是一个复杂的非线性过程，具有许多相互关联的子处理。人工神经网络可能非常适合对铝电解过程进行建模，但是此过程的安全性最关键的性质需要值得信赖的模型。在这项工作中，稀疏的神经网络经过训练，以建模铝电解模拟器的系统动力学。与相应的密集神经网络相比，稀疏模型结构的模型复杂性显着降低。我们认为这使模型更容易解释。此外，实证研究表明，稀疏模型比密集的神经网络从小型训练集中概括得更好。此外，训练具有不同参数初始化的稀疏神经网络的合奏表明，模型会收敛到具有相似学习的输入特征的相似模型结构。

基于物理学的模型已成为流体动力学的主流，用于开发预测模型。近年来，由于数据科学，处理单元，基于神经网络的技术和传感器适应性的快速发展，机器学习为流体社区提供了复兴。到目前为止，在流体动力学中的许多应用中，机器学习方法主要集中在标准过程上，该过程需要将培训数据集中在指定机器或数据中心上。在这封信中，我们提出了一种联合机器学习方法，该方法使本地化客户能够协作学习一个汇总和共享的预测模型，同时将所有培训数据保留在每个边缘设备上。我们证明了这种分散学习方法的可行性和前景，并努力为重建时空领域建立深度学习的替代模型。我们的结果表明，联合机器学习可能是设计与流体动力学相关的高度准确预测分散的数字双胞胎的可行工具。

即将到来的技术，例如涉及安全至关重要应用的数字双胞胎，自主和人工智能系统，需要准确，可解释，计算上有效且可推广的模型。不幸的是，两种最常用的建模方法，基于物理学的建模（PBM）和数据驱动的建模（DDM）无法满足所有这些要求。在当前的工作中，我们演示了将最佳PBM和DDM结合的混合方法如何导致模型可以胜过两者的模型。我们这样做是通过基于第一原则与黑匣子DDM相结合的偏微分方程，在这种情况下，深度神经网络模型补偿了未知物理。首先，我们提出了一个数学论点，说明为什么这种方法应该起作用，然后将混合方法应用于未知的源项模拟二维热扩散问题。结果证明了该方法在准确性和概括性方面的出色性能。此外，它显示了如何在混合框架中解释DDM部分以使整体方法可靠。

AutoEncoder技术在减少秩序建模中发现越来越常见的用途作为创建潜在空间的手段。这种缩小的订单表示为与时间序列预测模型集成时的非线性动力系统提供了模块化数据驱动建模方法。在这封信中，我们提出了一个非线性适当的正交分解（POD）框架，它是一个端到端的Galerkin的模型，组合AutoEncoders，用于动态的长短期内存网络。通过消除由于Galerkin模型的截断导致的投影误差，所提出的非流体方法的关键推动器是在POD系数的全级扩展和动态发展的潜空间之间的非线性映射的运动结构。我们测试我们的模型减少对流主导系统的框架，这通常是针对减少订单模型的具有挑战性。我们的方法不仅提高了准确性，而且显着降低了培训和测试的计算成本。

The hydrodynamic performance of a sea-going ship varies over its lifespan due to factors like marine fouling and the condition of the anti-fouling paint system. In order to accurately estimate the power demand and fuel consumption for a planned voyage, it is important to assess the hydrodynamic performance of the ship. The current work uses machine-learning (ML) methods to estimate the hydrodynamic performance of a ship using the onboard recorded in-service data. Three ML methods, NL-PCR, NL-PLSR and probabilistic ANN, are calibrated using the data from two sister ships. The calibrated models are used to extract the varying trend in ship's hydrodynamic performance over time and predict the change in performance through several propeller and hull cleaning events. The predicted change in performance is compared with the corresponding values estimated using the fouling friction coefficient ($\Delta C_F$). The ML methods are found to be performing well while modelling the hydrodynamic state variables of the ships with probabilistic ANN model performing the best, but the results from NL-PCR and NL-PLSR are not far behind, indicating that it may be possible to use simple methods to solve such problems with the help of domain knowledge.

在这项工作中，我们介绍，证明并展示了纠正源期限方法（Costa） - 一种新的混合分析和建模（火腿）的新方法。 HAM的目标是将基于物理的建模（PBM）和数据驱动的建模（DDM）组合，以创建概括，值得信赖，准确，计算高效和自我不断发展的模型。 Costa通过使用深神经网络产生的纠正源期限增强PBM模型的控制方程来实现这一目标。在一系列关于一维热扩散的数值实验中，发现CostA在精度方面优于相当的DDM和PBM模型 - 通常通过几个数量级降低预测误差 - 同时也比纯DDM更好地概括。由于其灵活而稳定的理论基础，Costa提供了一种模块化框架，用于利用PBM和DDM中的新颖开发。其理论基础还确保了哥斯达队可以用来模拟由（确定性）部分微分方程所控制的任何系统。此外，Costa有助于在PBM的背景下解释DNN生成的源术语，这导致DNN的解释性改善。这些因素使哥斯达成为数据驱动技术的潜在门开启者，以进入先前为纯PBM保留的高赌注应用。

Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is one of the main building blocks that strives to capture long-range dependencies, compared to the local convolutional-based design. However, the self-attention operation has quadratic complexity which proves to be a computational bottleneck, especially in volumetric medical imaging, where the inputs are 3D with numerous slices. In this paper, we propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters and compute cost. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features using a pair of inter-dependent branches based on spatial and channel attention. Our spatial attention formulation is efficient having linear complexity with respect to the input sequence length. To enable communication between spatial and channel-focused branches, we share the weights of query and key mapping functions that provide a complimentary benefit (paired attention), while also reducing the overall network parameters. Our extensive evaluations on three benchmarks, Synapse, BTCV and ACDC, reveal the effectiveness of the proposed contributions in terms of both efficiency and accuracy. On Synapse dataset, our UNETR++ sets a new state-of-the-art with a Dice Similarity Score of 87.2%, while being significantly efficient with a reduction of over 71% in terms of both parameters and FLOPs, compared to the best existing method in the literature. Code: https://github.com/Amshaker/unetr_plus_plus.