Computational simulation of hydraulic fracturing nonlinear dynamics using Gaussian processes surrogates Zio, Souleymane; Rochinha, Fernando A.
High-Fidelity physics based computational models enables the design and optimization of complex engineered processes. Moreover, important and strategic decisions might be taken relying on those computational models predictions. Therefore, there is a need for improving their robustness and reliability. Therefore, understanding the impacts on the predictions due to unavoidable input and model structures uncertainties, often referred to as Uncertainty Quantification (UQ), has become a major issue. A key aspect in this context is the demand of a significant computational effort involving many-queries of a computer code. That might be lessen by the use of reduced order models or any form of surrogates. Here, we employ Gaussian Processes (GPs) as a surrogate (often referred to as emulators) for a computer code devoted to Hydraulic Fracturing simulation.
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