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Filippo Giorcelli, Sergej Antonello Sirigu, Giuseppe Giorgi, Nicolás Faedo, Mauro Bonfanti, Jacopo Ramello, Ermanno Giorcelli and Giuliana Mattiazzo
Among the challenges generated by the global climate crisis, a significant concern is the constant increase in energy demand. This leads to the need to ensure that any novel energy systems are not only renewable but also reliable in their performance. A ...
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Haojie Lian, Xinhao Li, Leilei Chen, Xin Wen, Mengxi Zhang, Jieyuan Zhang and Yilin Qu
Neural radiance fields and neural reflectance fields are novel deep learning methods for generating novel views of 3D scenes from 2D images. To extend the neural scene representation techniques to complex underwater environments, beyond neural reflectanc...
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Jiangtao Chen, Jiao Zhao, Wei Xiao, Luogeng Lv, Wei Zhao and Xiaojun Wu
Given the randomness inherent in fluid dynamics problems and limitations in human cognition, Computational Fluid Dynamics (CFD) modeling and simulation are afflicted with non-negligible uncertainties, casting doubts on the credibility of CFD. Scientifica...
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Chen Xia, Christian Eduardo Verdonk Gallego, Adrián Fabio Bracero, Víctor Fernando Gómez Comendador and Rosa María Arnaldo Valdés
This paper investigates the impact of trajectory predictor performance on the encounter probability generated by an adaptive conflict detection tool and examines the flexibility of the tool dependent on its adjustable thresholds, using historical radar t...
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Kate Carlson, Barbara P. Buttenfield and Yi Qiang
Quantification of all types of uncertainty helps to establish reliability in any analysis. This research focuses on uncertainty in two attribute levels of wetland classification and creates visualization tools to guide analysis of spatial uncertainty pat...
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Z. Jason Hou, Nicholas D. Ward, Allison N. Myers-Pigg, Xinming Lin, Scott R. Waichler, Cora Wiese Moore, Matthew J. Norwood, Peter Regier and Steven B. Yabusaki
The influence of coastal ecosystems on global greenhouse gas (GHG) budgets and their response to increasing inundation and salinization remains poorly constrained. In this study, we have integrated an uncertainty quantification (UQ) and ensemble machine ...
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Xuan Di, Rongye Shi, Zhaobin Mo and Yongjie Fu
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DN...
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Mingzhi Li, Xianjun Yu, Dejun Meng, Guangfeng An and Baojie Liu
Studies on the geometry variation-related compressor uncertainty quantification (UQ) have often used dimension reduction methods, such as the principal component analysis (PCA), for the modeling of deviations. However, in the PCA method, the main eigenmo...
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Shenren Xu, Qian Zhang, Dingxi Wang and Xiuquan Huang
Precise and inexpensive uncertainty quantification (UQ) is crucial for robust optimization of compressor blades and to control manufacturing tolerances. This study looks into the suitability of MC-adj-nonlinear, a nonlinear adjoint-based approach, to pre...
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Andreas Nugaard Holm, Dustin Wright and Isabelle Augenstein
Uncertainty approximation in text classification is an important area with applications in domain adaptation and interpretability. One of the most widely used uncertainty approximation methods is Monte Carlo (MC) dropout, which is computationally expensi...
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