Geoscience Data Journal, 2022 The current handling of data in earth observation, modelling and prediction measures gives cause for critical consideration, since we all too often carelessly ignore data uncertainty.
Hendrik Paasche +4 more
doaj +1 more source
Coupling Design and Validation Analysis of an Integrated Framework of Uncertainty Quantification
Energies, 2023 The uncertainty quantification is an indispensable part for the validation of the nuclear safety best-estimate codes. However, the uncertainty quantification usually requires the combination of statistical analysis software and nuclear reactor ...
Bo Pang +7 more
doaj +1 more source
Convex Optimal Uncertainty Quantification [PDF]
, 2015 Optimal uncertainty quantification (OUQ) is a framework for numerical extreme-case analysis of stochastic systems with imperfect knowledge of the underlying probability distribution.
Han, Shuo +4 more
core +3 more sources
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis [PDF]
Conference on Empirical Methods in Natural Language Processing, 2022 Pre-trained language models (PLMs) have gained increasing popularity due to their compelling prediction performance in diverse natural language processing (NLP) tasks. When formulating a PLM-based prediction pipeline for NLP tasks, it is also crucial for
Yuxin Xiao +5 more
semanticscholar +1 more source
Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification [PDF]
European Conference on Computer Vision, 2022 A critical limitation of current methods based on Neural Radiance Fields (NeRF) is that they are unable to quantify the uncertainty associated with the learned appearance and geometry of the scene.
Jianxiong Shen +3 more
semanticscholar +1 more source
Bi-level Hybrid Uncertainty Quantification in Fatigue Analysis: S-N Curve Approach
Fracture and Structural Integrity, 2020 Due to its physical complexity, fatigue phenomenon inherently presents a significant number of uncertain parameters to be predicted. In uncertainty quantification (UQ), research has demonstrated that even a small variation in uncertain input quantities ...
Raphael Basilio Pires Nonato
doaj +3 more sources
Deep evidential learning in diffusion convolutional recurrent neural network
Electronic Research Archive, 2023 Graph neural networks (GNNs) is applied successfully in many graph tasks, but there still exists a limitation that many of GNNs model do not consider uncertainty quantification of its output predictions.
Zhiyuan Feng +5 more
doaj +1 more source
Uncertainty Quantification of Collaborative Detection for Self-Driving [PDF]
IEEE International Conference on Robotics and Automation, 2022 Sharing information between connected and autonomous vehicles (CAVs) fundamentally improves the performance of collaborative object detection for self-driving.
Sanbao Su +6 more
semanticscholar +1 more source
UQpy v4.1: Uncertainty quantification with Python
SoftwareX, 2023 This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured to conform with the latest Python coding conventions, refactored to ...
Dimitrios Tsapetis +11 more
doaj +1 more source
Towards Best Practice Framing of Uncertainty in Scientific Publications: A Review of Water Resources Research Abstracts [PDF]
, 2017 Uncertainty is recognized as a key issue in water resources research, amongst other sciences. Discussions of uncertainty typically focus on tools and techniques applied within an analysis, e.g. uncertainty quantification and model validation.
Elsawah, Sondoss +4 more
core +1 more source