Pages that link to "Item:Q2126979"
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The following pages link to Physics-informed machine learning with conditional Karhunen-Loève expansions (Q2126979):
Displaying 11 items.
- Use of multifidelity training data and transfer learning for efficient construction of subsurface flow surrogate models (Q2112502) (← links)
- Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs (Q2133772) (← links)
- Conditional physics informed neural networks (Q2247060) (← links)
- Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems (Q2671323) (← links)
- Learning to Predict Physical Properties using Sums of Separable Functions (Q3116493) (← links)
- Conditional Karhunen-Loève regression model with basis adaptation for high-dimensional problems: uncertainty quantification and inverse modeling (Q6118544) (← links)
- Improved training of physics-informed neural networks for parabolic differential equations with sharply perturbed initial conditions (Q6171154) (← links)
- Physics-informed machine learning method with space-time Karhunen-Loève expansions for forward and inverse partial differential equations (Q6196622) (← links)
- Physics-Informed Machine Learning with Conditional Karhunen-Lo\`eve Expansions (Q6330452) (← links)
- Random field of homogeneous and multi-material structures by the smoothed finite element method and Karhunen-Loève expansion (Q6578060) (← links)
- Randomized physics-informed machine learning for uncertainty quantification in high-dimensional inverse problems (Q6639314) (← links)
