Results 71 to 80 of about 14,505 (295)

Interval-Valued Uncertainty Based on Entropy and Dempster–Shafer Theory

Journal of Statistical Theory and Applications (JSTA), 2018
This paper presents a new structure as a simple method at two uncertainties (i.e., aleatory and epistemic) that result from variabilities inherent in nature and a lack of knowledge.
F. Khalaj, E. Pasha, R. Tavakkoli-Moghaddam, M. Khalaj   +3 more
doaj   +1 more source

Uncertainty‐Aware Deep Ensembles for Robust and Reliable Chemical Sensor Arrays

Advanced Science, EarlyView.
A reliability‐aware electronic nose is developed using photothermally anchored metal‐catalyst decorated metal oxide nanofiber sensor arrays combined with deep ensemble learning. Diverse catalytic nanofiber channels generate gas‐specific response patterns, enabling selective identification and quantification of sulfur‐containing gases.
Sungwoo Eo, Ji‐Hwan Eum, Suk‐Jeong Kwon, Minji Sagong, Dongchan Kim, Dong‐Ha Kim   +5 more
wiley   +1 more source

Informing synthetic passive microwave predictions through Bayesian deep learning with uncertainty decomposition

Environmental Data Science
Space-borne passive microwave (PMW) data provide rich information on atmospheric state, including cloud structure and underlying surface properties. However, PMW data are sparse and limited due to low Earth orbit collection, resulting in coarse Earth ...
Pedro Ortiz, Eleanor Casas, Marko Orescanin, Scott W. Powell, Veljko Petkovic   +4 more
doaj   +1 more source

On the colour and spin of epistemic error (and what we might do about it) [PDF]

, 2011
Disinformation as a result of epistemic error is an issue in hydrological modelling. In particular the way in which the colour in model residuals resulting from epistemic errors should be expected to be non-stationary means that it is difficult to ...
A. Wood, Wood, Andrew, Smith, P. J., Beven, Keith, Smith, Paul, P. J. Smith, Wood, A., K. Beven   +7 more
core   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Mixed uncertainty quantification of ship non-contact underwater explosion system

Zhongguo Jianchuan Yanjiu
ObjectivesSubstantial epistemic and aleatory uncertainties coexist in the modeling and simulation (M&S) of ship non-contact underwater explosion (UNDEX).
Xiao LIANG, Yiyi YANG, Jiangtao CHEN, Ruili WANG   +3 more
doaj   +1 more source

The expression of certainty and uncertainty in Italian speaking children

Ricerche di Pedagogia e Didattica, 2014
Epistemic modality expresses the speaker’s attitude of certainty/uncertainty toward the fact/event mentioned in the proposition. In conversation, the use of epistemic modality informs the listener on how much s/he should rely on the pieceof information ...
Maria Silvia Barbieri
doaj   +1 more source

Dictionary‐based weak‐form training for noise‐robust series hybrid models with multiplicative unknowns

AIChE Journal, EarlyView.
ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho, Akshay Kudva, Parth Shah, Dongheon Lee, Joseph Sang‐Il Kwon   +4 more
wiley   +1 more source

Taguchi–Bayesian Sampling: A Roadmap for Polymer Database Construction Toward Small Representative Machine Learning

Advanced Intelligent Discovery, EarlyView.
This article establishes a Taguchi–Bayesian sampling strategy to reconstruct polymer processing–property landscape at minimal sampling cost, generically building the roadmap for materials database construction from sampling their vast design space. This sampling strategy is featured by an alternating lesson between uniformity and representativeness ...
Han Liu, Liantang Li
wiley   +1 more source

When clients (or psychotherapists) say “I don’t know”: unknowledge or uncertainty?

Gestalt Theory
Viewing psychotherapy conversations from an epistemic perspective involves analysing how psychotherapist and client manage their knowledge, insufficient knowledge (uncertainty) and lack of knowledge (unknowledge).
Zuczkowski Andrzej, Stemberger Gerhard
doaj   +1 more source