Sentences with phrase «uncertainty quantification»

"Uncertainty quantification" refers to the process of measuring and understanding the extent of uncertainty or unpredictability in a given situation or problem. It involves using mathematical or statistical methods to determine the range of possible outcomes and their likelihood. In simpler terms, it's about assessing and quantifying how unsure or unsure we are about something. Full definition
I furthermore use techniques from uncertainty quantification in inverse problems to assess observation systems for the reconstruction of modern and past ocean states.
I and my colleagues were pursuing uncertainty quantification for climate change in collaboration with other DOE labs.
I use ocean and adjoint modelling as well as tools from uncertainty quantification to study sensitivities and uncertainties of ocean circulation.
In a paper published last December in the SIAM Journal on Uncertainty Quantification, authors Matthew Heaton, Tamara Greasby, and Stephan Sain propose a statistical hierarchical Bayesian model that consolidates climate change information from observation - based data sets and climate models.
Gary Geernaert, director of DOE's Climate and Environmental Sciences Division, states that «it is critical that federal investments to advance climate science for use by both public and private stakeholders must place significant priority on incorporating uncertainty quantification methodologies into modeling frameworks.
If climate folks want to interface with math and stat people studying uncertainty quantification, here is a good opportunity:
Using a unique uncertainty quantification (UQ) technique, scientists at Pacific Northwest National Laboratory, Scripps Institution of Oceanography, and Nanjing University...
Rapid estimation of global civil aircraft emissions with uncertainty quantification.
This guidance document aims to promote uncertainty quantification, because quantifying uncertainty has the potential to foster more robust climate - change mitigation plans.
One colleague later remarked that her approach to uncertainty quantification reminded him of an English major who had just finished reading Kuhn's «The Structure of Scientific Revolutions.
Dr. Yun Qian, atmospheric and climate modeling scientist at Pacific Northwest National Laboratory, was invited to organize and direct an international workshop on «Uncertainty Quantification in Climate Modeling and Projection» in Trieste, Italy.
The current paper, «Expert judgement and uncertainty quantification for climate change,» was published online April 27 by Nature Climate Change.
In recognition of his leadership and expertise in climate research, modeling, and uncertainty quantification (UQ), both organizations and the IUGG, supported Qian.
Uncertainty quantification is also a focus for the U.S. Department of Energy (DOE) as eight national laboratories and six partner institutions collaborate to develop and apply the next generation of climate and Earth - system models to the challenges and demands of climate - change research.
Uncertainty Quantification (UQ) entails much more than attaching defensible error bars to predictions.
This environment, designated the Virtual Environment for Reactor Applications (VERA), incorporates science - based models, state - of - the - art numerical methods, modern computational science and engineering practices, and uncertainty quantification (UQ) and validation against data from operating pressurized water reactors (PWRs), single - effect experiments, and integral tests.
«Uncertainty quantification» (or «UQ» for short) is a research area that has sprung to prominence in the last decade at the interface of applied mathematics, statistics, computational science, and many applications, usually in physical sciences and engineering, but also in biology, finance, and insurance.
Atmospheric and computational researchers at Pacific Northwest National Laboratory used a new scientific approach called «uncertainty quantification,» or UQ, that allowed...
Conference topics of emphasis will include dynamics, high performance computing, numerical analysis, cloud systems behavior, data assimilation, dimension reduction, uncertainty quantification, model hierarchy, and statistical approaches.
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