Pages that link to "Item:Q2319398"
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The following pages link to Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems (Q2319398):
Displaying 22 items.
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification (Q1721865) (← links)
- A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness (Q1987969) (← links)
- Systems of Gaussian process models for directed chains of solvers (Q1988026) (← links)
- Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold (Q2020284) (← links)
- An encoder-decoder deep surrogate for reverse time migration in seismic imaging under uncertainty (Q2027200) (← links)
- Interpretable machine learning: fundamental principles and 10 grand challenges (Q2074414) (← links)
- A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables (Q2124009) (← links)
- Probabilistic deep learning for real-time large deformation simulations (Q2160483) (← links)
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data (Q2222275) (← links)
- A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the small data regime (Q2222510) (← links)
- Theory-guided auto-encoder for surrogate construction and inverse modeling (Q2237777) (← links)
- Robust topology optimization with low rank approximation using artificial neural networks (Q2667321) (← links)
- Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
- Isogeometric neural networks: a new deep learning approach for solving parameterized partial differential equations (Q2683423) (← links)
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- Bayesian differential programming for robust systems identification under uncertainty (Q5161165) (← links)
- Deep capsule encoder–decoder network for surrogate modeling and uncertainty quantification (Q6082494) (← links)
- Fully probabilistic deep models for forward and inverse problems in parametric PDEs (Q6095115) (← links)
- Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets (Q6159313) (← links)
- Physics-aware neural implicit solvers for multiscale, parametric PDEs with applications in heterogeneous media (Q6641874) (← links)
- A review of recent advances in surrogate models for uncertainty quantification of high-dimensional engineering applications (Q6663327) (← links)
