A probabilistic framework for multidisciplinary design: application to the hydrostructural optimization of supercavitating hydrofoils
DOI10.1002/NME.5923zbMATH Open1548.76025MaRDI QIDQ6555245
G. E. Karniadakis, Jose del Águila, Luca Bonfiglio, Paris Perdikaris
Publication date: 14 June 2024
Published in: International Journal for Numerical Methods in Engineering (Search for Journal in Brave)
fluid-structure interactionmachine learningmultiphysicssupercavitationmultifidelityBayesian optimization
Bayesian problems; characterization of Bayes procedures (62C10) Fluid-solid interactions (including aero- and hydro-elasticity, porosity, etc.) (74F10) Jets and cavities, cavitation, free-streamline theory, water-entry problems, airfoil and hydrofoil theory, sloshing (76B10)
Cites Work
- Title not available (Why is that?)
- On the limited memory BFGS method for large scale optimization
- Reduced-order fluid/structure modeling of a complete aircraft configuration
- Volume of fluid (VOF) method for the dynamics of free boundaries
- The numerical computation of turbulent flows
- A preconditioned Navier-Stokes method for two-phase flows with application to cavitation prediction
- Bayesian learning for neural networks
- Supremizer stabilization of POD-Galerkin approximation of parametrized steady incompressible Navier-Stokes equations
- Posterior consistency for Gaussian process approximations of Bayesian posterior distributions
- Predicting the output from a complex computer code when fast approximations are available
- RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY
- Advanced Lectures on Machine Learning
- Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets
This page was built for publication: A probabilistic framework for multidisciplinary design: application to the hydrostructural optimization of supercavitating hydrofoils
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6555245)
