Pages that link to "Item:Q2664065"

The following pages link to Data-driven surrogate modeling of multiphase flows using machine learning techniques (Q2664065):

Displaying 7 items.

• Use of multifidelity training data and transfer learning for efficient construction of subsurface flow surrogate models (Q2112502) (← links)
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
• A projection-based, semi-implicit time-stepping approach for the Cahn-Hilliard Navier-Stokes equations on adaptive octree meshes (Q2683096) (← links)
• Multi-Fidelity Machine Learning Applied to Steady Fluid Flows (Q5880416) (← links)
• Blending machine learning and sequential data assimilation over latent spaces for surrogate modeling of Boussinesq systems (Q6160016) (← links)
• Fundamentals of Data-Driven Surrogate Modeling (Q6177991) (← links)
• Three dimensional interface normal prediction for volume-of-fluid method using artificial neural network (Q6572762) (← links)