A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics
Giulio Ortali, Mathematics Area, mathLab, SISSA, via Bonomea 265, I-34136 Trieste, Italy, Nicola Demo, Gianluigi Rozza
Abstract
<abstract><p>This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR). This approach is applied initially to a literature case, the simulation of the Stokes problem, and in the following to a real-world industrial problem, within a shape optimization pipeline for a naval engineering problem.</p></abstract>
Topics & Concepts
KrigingProper orthogonal decompositionGaussian processPipeline (software)Partial differential equationApplied mathematicsProcess (computing)RegressionPoint of deliveryMathematicsMathematical optimizationReduction (mathematics)GaussianComputer scienceStatisticsMathematical analysisPhysicsOperating systemGeometryQuantum mechanicsBiologyProgramming languageAgronomyModel Reduction and Neural NetworksOil and Gas Production TechniquesProbabilistic and Robust Engineering Design