Error modeling for surrogates of dynamical systems using machine learning
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Publication:6557511
DOI10.1002/nme.5583MaRDI QIDQ6557511
Sumeet Trehan, Louis J. Durlofsky, Kevin T. Carlberg
Publication date: 18 June 2024
Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)
nonlinear dynamical systembottom-hole pressure controlreduced-order nonlinear oil-water flow modeltime-dependent surrogate-model errortraining parameter set
Learning and adaptive systems in artificial intelligence (68T05) Hydrology, hydrography, oceanography (86A05) Dynamical systems in fluid mechanics, oceanography and meteorology (37N10) Basic methods in fluid mechanics (76M99)
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Related Items (4)
Adaptive reduced order modeling for nonlinear dynamical systems through a new a posteriori error estimator: application to uncertainty quantification ⋮ A dynamical systems approach to machine learning ⋮ Reduced order modeling of random linear dynamical systems based on a new a posteriori error bound ⋮ Learning about structural errors in models of complex dynamical systems
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