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Bayesian spatiotemporal modeling for inverse problems
Inverse problems with spatiotemporal observations are ubiquitous in scientific studies and engineering applications. In these spatiotemporal inverse...
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Regularising Inverse Problems with Generative Machine Learning Models
Deep neural network approaches to inverse imaging problems have produced impressive results in the last few years. In this survey paper, we consider...
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Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems
Neural networks have become a prominent approach to solve inverse problems in recent years. While a plethora of such methods was developed to solve...
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A novel normalized reduced-order physics-informed neural network for solving inverse problems
The utilization of Physics-informed Neural Networks (PINNs) in deciphering inverse problems has gained significant attention in recent years....
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Hierarchical dynamic workload scheduling on heterogeneous clusters for grid search of inverse problems
Inverse problems occur in many scientific fields. Albeit grid search, where points of a regular grid are tested as possible solutions, is a...
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Gaussian processes for Bayesian inverse problems associated with linear partial differential equations
This work is concerned with the use of Gaussian surrogate models for Bayesian inverse problems associated with linear partial differential equations....
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Learning Posterior Distributions in Underdetermined Inverse Problems
In recent years, classical knowledge-driven approaches for inverse problems have been complemented by data-driven methods exploiting the power of...
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Learning mean curvature-based regularization to solve the inverse variational problems from noisy data
As an emerging mathematical tool, inverse variational problem approximation (IVPA) has some real applications. Recently, deep learning is used to...
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Proximal Residual Flows for Bayesian Inverse Problems
Normalizing flows are a powerful tool for generative modelling, density estimation and posterior reconstruction in Bayesian inverse problems. In this...
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Discovery the inverse variational problems from noisy data by physics-constrained machine learning
Almost sophisticated physical phenomena and computational problems arise as variational problems. Recently, the development of neural networks (NNs),...
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Parallel Operator Splitting Algorithms with Application to Imaging Inverse Problems
Image denoising, image deblurring, image inpainting, super-resolution, and compressed sensing reconstruction have important application value in...
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A data-driven and model-based accelerated Hamiltonian Monte Carlo method for Bayesian elliptic inverse problems
In this paper, we consider a Bayesian inverse problem modeled by elliptic partial differential equations (PDEs). Specifically, we propose a...
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An Ulm-like algorithm for generalized inverse eigenvalue problems
In this paper, we study the numerical solutions of the generalized inverse eigenvalue problem (for short, GIEP). Motivated by Ulm’s method for...
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Computational Efficiency of Iterative Methods for Solving Inverse Problems
The article is concerned with develo** effective methods for solving inverse problems of wave tomography. The underlying mathematical model...
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Fast Bayesian inversion for high dimensional inverse problems
We investigate the use of learning approaches to handle Bayesian inverse problems in a computationally efficient way when the signals to be inverted...
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Application of Support Vector Machines in Inverse Problems in Ocean Color Remote Sensing
Neural networks are widely used as transfer functions in inverse problems in remote sensing. However, this method still suffers from some problems...
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Cost free hyper-parameter selection/averaging for Bayesian inverse problems with vanilla and Rao-Blackwellized SMC samplers
In Bayesian inverse problems, one aims at characterizing the posterior distribution of a set of unknowns, given indirect measurements. For...
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GCGE: a package for solving large scale eigenvalue problems by parallel block dam** inverse power method
In this paper, we introduce some strategies to improve the efficiency and scalability of the generalized conjugate gradient algorithm and build a...
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Towards Off-the-Grid Algorithms for Total Variation Regularized Inverse Problems
We introduce an algorithm to solve linear inverse problems regularized with the total (gradient) variation in a gridless manner. Contrary to most...
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Proximal algorithm for minimization problems in l0-regularization for nonlinear inverse problems
In this paper, we study a proximal method for the minimization problem arising from l 0 -regularization for nonlinear inverse problems. First of all,...
