Pages that link to "Item:Q2184449"
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The following pages link to A bi-fidelity surrogate modeling approach for uncertainty propagation in three-dimensional hemodynamic simulations (Q2184449):
Displaying 13 items.
- Propagating uncertainties in large-scale hemodynamics models via network uncertainty quantification and reduced-order modeling (Q1989088) (← links)
- Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling (Q1996002) (← links)
- Transfer learning based multi-fidelity physics informed deep neural network (Q2127006) (← links)
- PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (Q2128357) (← links)
- Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics (Q2184337) (← links)
- Learning nonlocal constitutive models with neural networks (Q2237430) (← links)
- A bi-fidelity ensemble Kalman method for PDE-constrained inverse problems in computational mechanics (Q2241887) (← links)
- Geometric uncertainty in patient-specific cardiovascular modeling with convolutional dropout networks (Q2246251) (← links)
- A generalized multi-resolution expansion for uncertainty propagation with application to cardiovascular modeling (Q2310367) (← links)
- Impact of geometric uncertainty on hemodynamic simulations using machine learning (Q2631533) (← links)
- A bi-fidelity stochastic collocation method for transport equations with diffusive scaling and multi-dimensional random inputs (Q2671327) (← links)
- Cardiovascular Modeling With Adapted Parametric Inference (Q4615456) (← links)
- Bifidelity Surrogate Modelling: Showcasing the Need for New Test Instances (Q5060781) (← links)
