Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data | Paper Citation Record

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This paper cites Inverse problem theory and methods for model parameter estimation.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Inverse problem theory and methods for model parameter estimation

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This paper cites An introduction to inverse problems with applications.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data An introduction to inverse problems with applications

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This paper cites An inverse problem approach for elasticity imaging through vibroacoustics.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data An inverse problem approach for elasticity imaging through vibroacoustics

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This paper cites Crop physiology cali- bration in clm.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Crop physiology cali- bration in clm

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This paper cites Bayesian model updating for struc- tural dynamic applications combing differential evolution adaptive metropolis and kriging model.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Bayesian model updating for struc- tural dynamic applications combing differential evolution adaptive metropolis and kriging model

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This paper cites Solving inverse problems using conditional invertible neural networks.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Solving inverse problems using conditional invertible neural networks

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This paper cites Inverse analysis of asteroseismic data: a review.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Inverse analysis of asteroseismic data: a review

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This paper cites Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides

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This paper cites Inverse problems: From regularization to bayesian inference.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Inverse problems: From regularization to bayesian inference

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This paper cites Inverse problems: a bayesian perspective.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Inverse problems: a bayesian perspective

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This paper cites The Bayesian Approach To Inverse Problems.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data The Bayesian Approach To Inverse Problems

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This paper cites Benchmarking simulation-based inference.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Benchmarking simulation-based inference

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This paper cites A recursive inference method based on invertible neural network for multi-level model updating using video monitoring data.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data A recursive inference method based on invertible neural network for multi-level model updating using video monitoring data

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This paper cites Time series analysis by state space methods , volume 38.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Time series analysis by state space methods , volume 38

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This paper cites State-of-the-art review on bayesian inference in structural system identification and damage assess- ment.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data State-of-the-art review on bayesian inference in structural system identification and damage assess- ment

Reference 15

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This paper cites Conditional karhunen–lo` eve regression model with basis adaptation for high-dimensional problems: Uncertainty quantification and inverse modeling.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Conditional karhunen–lo` eve regression model with basis adaptation for high-dimensional problems: Uncertainty quantification and inverse modeling

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This paper cites A survey on high-dimensional gaussian process modeling with application to bayesian optimization.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data A survey on high-dimensional gaussian process modeling with application to bayesian optimization

Reference 17

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This paper cites Machine learning enabled fusion of cae data and test data for vehicle crashworthiness performance evaluation by analysis.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Machine learning enabled fusion of cae data and test data for vehicle crashworthiness performance evaluation by analysis

Reference 18

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This paper cites Dimensionality reduction and polynomial chaos acceleration of bayesian inference in inverse problems.Journal of Computational Physics, 228(6):1862–1902, 2009.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Dimensionality reduction and polynomial chaos acceleration of bayesian inference in inverse problems.Journal of Computational Physics, 228(6):1862–1902, 2009

Reference 19

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This paper cites Adaptive multi-fidelity polynomial chaos approach to bayesian inference in inverse problems.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Adaptive multi-fidelity polynomial chaos approach to bayesian inference in inverse problems

Reference 20

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This paper cites Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification

Reference 21

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This paper cites Nett: Solv- ing inverse problems with deep neural networks.Inverse Problems, 36(6):065005, 2020.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Nett: Solv- ing inverse problems with deep neural networks.Inverse Problems, 36(6):065005, 2020

Reference 22

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This paper cites Approximate bayesian computation.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Approximate bayesian computation

Reference 23

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This paper cites Bayesflow: Learning complex stochastic models with invertible neural networks.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Bayesflow: Learning complex stochastic models with invertible neural networks

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This paper cites Density estimation using deep generative neural networks.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Density estimation using deep generative neural networks

Reference 25

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This paper cites Flow-GAN: Combining maximum likelihood and adversarial learning in generative models.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Flow-GAN: Combining maximum likelihood and adversarial learning in generative models

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This paper cites Solution of physics-based bayesian inverse problems with deep generative priors.Computer Methods in Applied Mechanics and Engineering, 400:115428, 2022.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Solution of physics-based bayesian inverse problems with deep generative priors.Computer Methods in Applied Mechanics and Engineering, 400:115428, 2022

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This paper cites Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

Reference 28

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This paper cites To- wards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data To- wards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother

Reference 29

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This paper cites Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference

Reference 30

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Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data GATSBI: Generative Adversarial Training for Simulation-Based Inference

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Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Bayesian Inference with Generative Adversarial Network Priors

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This paper cites Pros and cons of gan evaluation measures: New developments.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Pros and cons of gan evaluation measures: New developments

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Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Normalizing flows for probabilistic modeling and inference

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This paper cites Estimation of agent-based models using bayesian deep learning ap- proach of bayesflow.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Estimation of agent-based models using bayesian deep learning ap- proach of bayesflow

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This paper cites Outbreakflow: Model-based bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the covid-19 pandemics in germany.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Outbreakflow: Model-based bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the covid-19 pandemics in germany

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This paper cites Noise-net: determining physical properties of h ii regions reflect- ing observational uncertainties.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Noise-net: determining physical properties of h ii regions reflect- ing observational uncertainties

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This paper cites Guided Image Generation with Conditional Invertible Neural Networks.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Guided Image Generation with Conditional Invertible Neural Networks

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Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Probabilistic damage detection using a new likelihood-free bayesian inference method

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This paper cites Doc- umentation for the skeletal storage, compaction, and subsidence (csub) package of modflow 6.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Doc- umentation for the skeletal storage, compaction, and subsidence (csub) package of modflow 6

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This paper cites A family of nonparametric density estimation algorithms.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data A family of nonparametric density estimation algorithms

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This paper cites Density estimation by dual ascent of the log-likelihood.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Density estimation by dual ascent of the log-likelihood

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Observation 0d659733-c3e7-41c8-bbb1-00ebee32f0f8 · outbound

This paper cites Normalizing flows: An introduction and review of current methods.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Normalizing flows: An introduction and review of current methods

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This paper cites Density estimation using Real NVP.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Density estimation using Real NVP

Reference 44

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This paper cites Neural spline flows.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Neural spline flows

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This paper cites An exercise in ground-water model calibration and prediction.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data An exercise in ground-water model calibration and prediction

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:29:12.894007Z

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This paper cites Bayesian reduced-order deep learning surrogate model for dynamic sys- tems described by partial differential equations.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Bayesian reduced-order deep learning surrogate model for dynamic sys- tems described by partial differential equations

Reference 47

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Observation 7574f63a-07d8-4000-b2db-34b65ccb81d3 · outbound

This paper cites A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions

Reference 48

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Observation 484bb510-1ebc-47d5-b25b-5c9e207cec92 · outbound

This paper cites an unresolved cited work.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-11T21:29:12.864442Z

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Observation 5cc7ee5b-fb69-4537-80f1-4c940644501b · outbound

This paper cites Adam: A method for stochastic optimization.

Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data Adam: A method for stochastic optimization

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:29:12.855579Z

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