An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change [PDF]
Earth System DynamicsThis study investigated the computational benefits of using multi-fidelity statistical estimation (MFSE) algorithms to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario ...
J. D. Jakeman +6 more
doaj +1 more source
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
Entropy, 2021 Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings.
Benjamin Kompa +2 more
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Uncertainty quantification of time-dependent quantities in a system with adjustable level of smoothness [PDF]
, 2021 We summarise the results of a computational study involved with Uncertainty Quantification (UQ) in a benchmark turbulent burner flame simulation. UQ analysis of this simulation enables one to analyse the convergence performance of one of the most widely ...
Thomas, Peter J. +2 more
core +1 more source
Automated 3D cytoplasm segmentation in soft X-ray tomography
iScienceSummary: Cells’ structure is key to understanding cellular function, diagnostics, and therapy development. Soft X-ray tomography (SXT) is a unique tool to image cellular structure without fixation or labeling at high spatial resolution and throughput ...
Ayse Erozan +3 more
doaj +1 more source
Principal component density estimation for scenario generation using normalizing flows
Data-Centric Engineering, 2022 Neural networks-based learning of the distribution of non-dispatchable renewable electricity generation from sources, such as photovoltaics (PV) and wind as well as load demands, has recently gained attention.
Eike Cramer +3 more
doaj +1 more source
Frontiers in Computational Neuroscience, 2017 Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs).
Pietro Quaglio +6 more
doaj +1 more source
Reduced Basis Methods for Uncertainty Quantification
, 2017 In this work we review a reduced basis method for the solution of uncertainty quantification problems. Based on the basic setting of an elliptic partial differential equation with random input, we introduce the key ingredients of the reduced basis method,
Quarteroni, Alfio +5 more
core +1 more source
Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0) [PDF]
Geoscientific Model Development, 2020 Geological uncertainty quantification is critical to subsurface modeling and prediction, such as groundwater, oil or gas, and geothermal resources, and needs to be continuously updated with new data.
Z. Yin, S. Strebelle, J. Caers
doaj +1 more source
Solid Pseudopapillary Neoplasm of the Pancreas in Children and Adolescents: Expert Recommendations
Pediatric Blood &Cancer, EarlyView.ABSTRACT Solid pseudopapillary neoplasm of the pancreas (SPN) is a rare low‐grade malignant exocrine pancreatic tumor, mostly discovered during the second decade of life in females, with a very good prognosis, provided microscopically complete surgical excision is achieved.
Sabine Irtan +18 more
wiley +1 more source
Uncertainty quantification in damage mechanics models
, 2023 Plechac, PetrComputational modeling of material response, at the macroscopic scale, under external stimuli, is paramount to predicting material failure in various structural applications.
Troy, Jerome
core +1 more source