Zobrazeno 1 - 10
of 29 636
pro vyhledávání: '"Uncertainty Propagation"'
Gas-fired units (GFUs) with rapid regulation capabilities are considered an effective tool to mitigate fluctuations in the generation of renewable energy sources and have coupled electricity power systems (EPSs) and natural gas systems (NGSs) more ti
Externí odkaz:
http://arxiv.org/abs/2408.08101
Close-proximity exploration of small celestial bodies is crucial for the comprehensive and accurate characterization of their properties. However, the complex and uncertain dynamical environment around them contributes to a rapid dispersion of uncert
Externí odkaz:
http://arxiv.org/abs/2408.05970
Autor:
Arnquist, I. J., Avignone III, F. T., Barabash, A. S., Barton, C. J., Bhimani, K. H., Blalock, E., Bos, B., Busch, M., Caldwell, T. S., Chan, Y. -D., Christofferson, C. D., Chu, P. -H., Clark, M. L., Cuesta, C., Detwiler, J. A., Efremenko, Yu., Ejiri, H., Elliott, S. R., Fuad, N., Giovanetti, G. K., Green, M. P., Gruszko, J., Guinn, I. S., Guiseppe, V. E., Haufe, C. R., Henning, R., Aguilar, D. Hervas, Hoppe, E. W., Hostiuc, A., Kidd, M. F., Kim, I., Kouzes, R. T., Lannen V, T. E., Li, A., López-Castaño, J. M., Martin, R. D., Massarczyk, R., Meijer, S. J., Oli, T. K., Paudel, L. S., Pettus, W., Poon, A. W. P., Radford, D. C., Reine, A. L., Rielage, K., Ruof, N. W., Schaper, D. C., Schleich, S. J., Tedeschi, D., Varner, R. L., Vasilyev, S., Watkins, S. L., Wilkerson, J. F., Wiseman, C., Xu, W., Yu, C. -H.
The background index is an important quantity which is used in projecting and calculating the half-life sensitivity of neutrinoless double-beta decay ($0\nu\beta\beta$) experiments. A novel analysis framework is presented to calculate the background
Externí odkaz:
http://arxiv.org/abs/2408.06786
The use of AI technologies is percolating into the secure development of software-based systems, with an increasing trend of composing AI-based subsystems (with uncertain levels of performance) into automated pipelines. This presents a fundamental re
Externí odkaz:
http://arxiv.org/abs/2407.14540
The Koopman Operator (KO) provides an analytical solution of dynamical systems in terms of orthogonal polynomials. This work exploits this representation to include the propagation of uncertainties, where the polynomials are modified to work with sto
Externí odkaz:
http://arxiv.org/abs/2407.20052
A multifidelity method for the nonlinear propagation of uncertainties in the presence of stochastic accelerations is presented. The proposed algorithm treats the uncertainty propagation (UP) problem by separating the propagation of the initial uncert
Externí odkaz:
http://arxiv.org/abs/2405.15993
Autor:
Thompson, Andrew
Machine learning (ML) models are increasingly being used in metrology applications. However, for ML models to be credible in a metrology context they should be accompanied by principled uncertainty quantification. This paper addresses the challenge o
Externí odkaz:
http://arxiv.org/abs/2404.11224
Current approaches to model-based offline Reinforcement Learning (RL) often incorporate uncertainty-based reward penalization to address the distributional shift problem. While these approaches have achieved some success, we argue that this penalizat
Externí odkaz:
http://arxiv.org/abs/2406.04088
Autor:
Kazma, Mohamad, Taha, Ahmad F.
The rapid increase in the integration of intermittent and stochastic renewable energy resources (RER) introduces challenging issues related to power system stability. Interestingly, identifying grid nodes that can best support stochastic loads from R
Externí odkaz:
http://arxiv.org/abs/2405.05028
Autor:
Thakur, Akshay, Chakraborty, Souvik
We propose a neural operator framework, termed mixture density nonlinear manifold decoder (MD-NOMAD), for stochastic simulators. Our approach leverages an amalgamation of the pointwise operator learning neural architecture nonlinear manifold decoder
Externí odkaz:
http://arxiv.org/abs/2404.15731