Pages that link to "Item:Q5162376"
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The following pages link to An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems (Q5162376):
Displaying 40 items.
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification (Q1721865) (← links)
- Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification (Q2002273) (← links)
- Goal-oriented a-posteriori estimation of model error as an aid to parameter estimation (Q2083651) (← links)
- Multifidelity data fusion in convolutional encoder/decoder networks (Q2099723) (← links)
- Variable-order approach to nonlocal elasticity: theoretical formulation, order identification via deep learning, and applications (Q2115574) (← links)
- Solving inverse problems using conditional invertible neural networks (Q2120777) (← links)
- Calibrate, emulate, sample (Q2123875) (← links)
- Multi-fidelity Bayesian neural networks: algorithms and applications (Q2124403) (← links)
- Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems (Q2129320) (← links)
- Image inversion and uncertainty quantification for constitutive laws of pattern formation (Q2131064) (← links)
- Bayesian inversion using adaptive polynomial chaos kriging within subset simulation (Q2133745) (← links)
- Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network (Q2157149) (← links)
- Bayesian inference of non-linear multiscale model parameters accelerated by a deep neural network (Q2175257) (← links)
- Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems (Q2214560) (← links)
- Gradient-free Stein variational gradient descent with kernel approximation (Q2235046) (← links)
- Theory-guided auto-encoder for surrogate construction and inverse modeling (Q2237777) (← links)
- Stein variational gradient descent with local approximations (Q2246277) (← links)
- MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources (Q2667288) (← links)
- Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks (Q2683056) (← links)
- Adaptive construction of surrogates for the Bayesian solution of inverse problems (Q2878948) (← links)
- An Acceleration Strategy for Randomize-Then-Optimize Sampling Via Deep Neural Networks (Q5079536) (← links)
- Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods (Q5090110) (← links)
- Cholesky-Based Experimental Design for Gaussian Process and Kernel-Based Emulation and Calibration (Q5163220) (← links)
- A Bayesian scheme for reconstructing obstacles in acoustic waveguides (Q6084626) (← links)
- Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems (Q6095075) (← links)
- Probabilistic neural data fusion for learning from an arbitrary number of multi-fidelity data sets (Q6096443) (← links)
- Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks (Q6109143) (← links)
- An Adaptive Non-Intrusive Multi-Fidelity Reduced Basis Method for Parameterized Partial Differential Equations (Q6110109) (← links)
- Adaptive Ensemble Kalman Inversion with Statistical Linearization (Q6111310) (← links)
- Sequential Model Correction for Nonlinear Inverse Problems (Q6144056) (← links)
- Residual-based error correction for neural operator accelerated Infinite-dimensional Bayesian inverse problems (Q6147083) (← links)
- Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction (Q6158090) (← links)
- Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events (Q6189161) (← links)
- Efficient multifidelity likelihood-free Bayesian inference with adaptive computational resource allocation (Q6202141) (← links)
- A MCMC method based on surrogate model and Gaussian process parameterization for infinite Bayesian PDE inversion (Q6553787) (← links)
- Bayesian model error method for the passive inverse scattering problem (Q6557680) (← links)
- Adaptive neural network surrogate model for solving the nonlinear elastic inverse problem via Bayesian inference (Q6583083) (← links)
- Bayesian inversion with neural operator (BINO) for modeling subdiffusion: forward and inverse problems (Q6593344) (← links)
- Auto-weighted Bayesian physics-informed neural networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution (Q6662478) (← links)
- Adaptive operator learning for infinite-dimensional Bayesian inverse problems (Q6669407) (← links)
