Pages that link to "Item:Q2124403"
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The following pages link to Multi-fidelity Bayesian neural networks: algorithms and applications (Q2124403):
Displaying 29 items.
- Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities (Q2072477) (← links)
- A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations (Q2083198) (← links)
- Multifidelity data fusion in convolutional encoder/decoder networks (Q2099723) (← links)
- Learning functional priors and posteriors from data and physics (Q2135824) (← links)
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data (Q2138799) (← links)
- Active learning with multifidelity modeling for efficient rare event simulation (Q2168325) (← links)
- A fast multi-fidelity method with uncertainty quantification for complex data correlations: application to vortex-induced vibrations of marine risers (Q2246346) (← links)
- A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335) (← links)
- Multi-fidelity surrogate modeling using long short-term memory networks (Q2678526) (← links)
- Bayesian physics informed neural networks for real-world nonlinear dynamical systems (Q2679296) (← links)
- Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
- Physics-informed neural networks combined with polynomial interpolation to solve nonlinear partial differential equations (Q2682670) (← links)
- (Q5053275) (← links)
- Algorithmic Learning Theory (Q5464519) (← links)
- Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems (Q6095075) (← links)
- Multifidelity Bayesian Experimental Design to Quantify Rare-Event Statistics (Q6131420) (← links)
- Variational inference in neural functional prior using normalizing flows: application to differential equation and operator learning problems (Q6132292) (← links)
- NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators (Q6154538) (← links)
- Bayesian deep learning for partial differential equation parameter discovery with sparse and noisy data (Q6186272) (← links)
- Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations (Q6187659) (← links)
- Resolution-independent generative models based on operator learning for physics-constrained Bayesian inverse problems (Q6194148) (← links)
- A latent variable approach for non-hierarchical multi-fidelity adaptive sampling (Q6202955) (← links)
- Correcting model misspecification in physics-informed neural networks (PINNs) (Q6497270) (← links)
- Multi-Fidelity Uncertainty Propagation Approach for Multi-Dimensional Correlated Flow Field Responses (Q6549081) (← links)
- A framework for strategic discovery of credible neural network surrogate models under uncertainty (Q6557831) (← links)
- An information field theory approach to Bayesian state and parameter estimation in dynamical systems (Q6560717) (← links)
- Bright-dark rogue wave transition in coupled ab system via the physics-informed neural networks method (Q6574264) (← links)
- A multifidelity machine learning based semi-Lagrangian finite volume scheme for linear transport equations and the nonlinear Vlasov-Poisson system (Q6644357) (← links)
- Auto-weighted Bayesian physics-informed neural networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution (Q6662478) (← links)
