Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review [PDF]
IEEE/CAA Journal of Automatica Sinica, 2023 Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and
Sibo Cheng +16 more
semanticscholar +1 more source
Impact of ploidy and pathogen life cycle on resistance durability
Peer Community Journal, 2021 The breeding of resistant hosts based on the gene-for-gene interaction is crucial to address epidemics of plant pathogens in agroecosystems. Resistant host deployment strategies are developed and studied worldwide to decrease the probability of ...
Saubin, Méline +4 more
doaj +1 more source
loopUI-0.1: indicators to support needs and practices in 3D geological modelling uncertainty quantification [PDF]
Geoscientific Model Development, 2022 To support the needs of practitioners regarding 3D geological modelling and uncertainty quantification in the field, in particular from the mining industry, we propose a Python package called loopUI-0.1 that provides a set of local and global indicators ...
G. Pirot +13 more
doaj +1 more source
Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis [PDF]
Atmospheric Chemistry and Physics, 2020 An estimated 0.5–1 billion people globally have inadequate intakes of selenium (Se), due to a lack of bioavailable Se in agricultural soils. Deposition from the atmosphere, especially through precipitation, is an important source of Se to soils. However,
A. Feinberg +8 more
doaj +1 more source
Uncertainty Quantification of Reactivity Scales [PDF]
ChemPhysChem, 2021 AbstractThe front cover artwork is provided by Prof. Ricardo A. Mata from the University of Göttingen. The image contrasts single‐valued reactivity parameters and distributions thereof. The latter allow chemists to better assess relative reactivity and, therefore, support synthesis planning.
Jonny Proppe, Johannes Kircher
openalex +5 more sources
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 Large Language Models (LLMs) show promising results in language generation and instruction following but frequently"hallucinate", making their outputs less reliable.
Jinhao Duan +7 more
semanticscholar +1 more source
An Accurate Sample Rejection Estimator of the Outage Probability With Equal Gain Combining
IEEE Open Journal of the Communications Society, 2020 We evaluate the outage probability (OP) for L-branch equal gain combining (EGC) receivers operating over fading channels, i.e., equivalently the cumulative distribution function (CDF) of the sum of the L channel envelopes.
Nadhir Ben Rached +3 more
doaj +1 more source
A Benchmark on Uncertainty Quantification for Deep Learning Prognostics [PDF]
Reliability Engineering & System Safety, 2023 Reliable uncertainty quantification on RUL prediction is crucial for informative decision-making in predictive maintenance. In this context, we assess some of the latest developments in the field of uncertainty quantification for prognostics deep ...
Luis Basora +3 more
semanticscholar +1 more source
IEEE transactions on industrial electronics (1982. Print), 2021 Predicting future capacities and remaining useful life (RUL) with uncertainty quantification is a key but challenging issue in the applications of battery health diagnosis and management.
Kailong Liu +3 more
semanticscholar +1 more source
Principal Uncertainty Quantification With Spatial Correlation for Image Restoration Problems [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Uncertainty quantification for inverse problems in imaging has drawn much attention lately. Existing approaches towards this task define uncertainty regions based on probable values per pixel, while ignoring spatial correlations within the image ...
Omer Belhasin +4 more
semanticscholar +1 more source