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Physics-informed deep learning for molecular solubility prediction: integrating thermodynamic constraints with neural network architectures. [PDF]
Sci RepAmiri M.
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Uncertainty reduction and quantification in computational thermodynamics
Computational Materials Science, 2022Richard Otis
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Efficient Frequency-Domain Uncertainty Quantification Using Parameterized Model Order Reduction
, 2022 A parameterized model order reduction technique is investigated for the efficient frequency-domain uncertainty quantification of circuits obtained by the Partial Element Equiv-alent Circuit method.
Francesco Ferranti +2 more
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FAST AND ACCURATE MODEL REDUCTION FOR SPECTRAL METHODS IN UNCERTAINTY QUANTIFICATION
International Journal for Uncertainty Quantification, 2016Roland Pulch, Joost Rommes
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Validation, Uncertainty Quantification and Uncertainty Reduction for a Shock Tube Simulation
18th AIAA Non-Deterministic Approaches Conference, 2016While we rely on simulations to predict the response of complex systems, we recognize that the models that underlie these simulations are never perfect. Comparison of simulations with experiments is an important tool for exposing limitations of models, and providing insights into which models need improvement.
Chanyoung Park +3 more
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Reliability Engineering and System SafetyWe introduce a method to construct a stochastic surrogate model from the results of dimensionality reduction in forward uncertainty quantification. The hypothesis is that the high-dimensional input augmented by the output of a computational model admits ...
Jungho Kim, Sang-Ri Yi, Ziqi Wang
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Quantification and Reduction of Uncertainties in 3D Stress Models
2020<p>The undisturbed stress state of a potential site for nuclear waste disposal is of key importance for the assessment of long-term stability of the geotechnical installations and for seismic hazard assessment. In particular, the stability of pre-existing faults within and near a repository can only be evaluated with the ...
Moritz Ziegler, Oliver Heidbach
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Model order reduction approach to uncertainty quantification in eddy current problems
, 2017 An approach based on Model Order Reduction and Polynomial Chaos Expansion is proposed for uncertainty quantification in eddy current problems due to randomness in material parameters.
Lorenzo Codecasa, Luca Di Rienzo
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Use of Emulator Methodology for Uncertainty Reduction Quantification
SPE Latin America and Caribbean Petroleum Engineering Conference, 2014Abstract In petroleum engineering, simulation models are used in the reservoir performance prediction and in the decision making process. These models are complex systems, typically characterized by a vast number of input parameters. Usually the physical state of the reservoir is highly uncertain, and thus the appropriate parameters of ...
C. Ferreira +3 more
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