Generalized Bayes approach to inverse problems with model misspecification [0.03%]
模型设定误差下的逆问题的广义Bayes方法
Youngsoo Baek,Wilkins Aquino,Sayan Mukherjee
Youngsoo Baek
We propose a general framework for obtaining probabilistic solutions to PDE-based inverse problems. Bayesian methods are attractive for uncertainty quantification but assume knowledge of the likelihood model or data generation process. This...
Riccardo Barbano,Željko Kereta,Andreas Hauptmann et al.
Riccardo Barbano et al.
Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities. These approaches usually require a large amount of high-quality paired training data, which is often not avai...
Blood and Breath Alcohol Concentration from Transdermal Alcohol Biosensor Data: Estimation and Uncertainty Quantification via Forward and Inverse Filtering for a Covariate-Dependent, Physics-Informed, Hidden Markov Model [0.03%]
基于协变量的物理信息隐马尔可夫模型下经皮酒精生物传感器数据的呼出和血液乙醇浓度估计及不确定性量化研究
Clemens Oszkinat,Tianlan Shao,Chunming Wang et al.
Clemens Oszkinat et al.
Transdermal alcohol biosensors that do not require active participation of the subject and yield near continuous measurements have the potential to significantly enhance the data collection abilities of alcohol researchers and clinicians wh...
An Extended Primal-Dual Algorithm Framework for Nonconvex Problems: Application to Image Reconstruction in Spectral CT [0.03%]
应用于光谱CT图像重建的非凸问题扩展对偶算法框架
Yu Gao,Xiaochuan Pan,Chong Chen
Yu Gao
Using the convexity of each component of the forward operator, we propose an extended primal-dual algorithm framework for solving a kind of nonconvex and probably nonsmooth optimization problems in spectral CT image reconstruction. Followin...
Deep neural-network based optimization for the design of a multi-element surface magnet for MRI applications [0.03%]
基于深度神经网络的优化设计多元件表面磁铁用于MRI应用中的研究
Sumit Tewari,Sahar Yousefi,Andrew Webb
Sumit Tewari
We present a combination of a CNN-based encoder with an analytical forward map for solving inverse problems. We call it an encoder-analytic (EA) hybrid model. It does not require a dedicated training dataset and can train itself from the co...
Chao Wang,Min Tao,Chen-Nee Chuah et al.
Chao Wang et al.
In this paper, we study the L 1 /L 2 minimization on the gradient for imaging applications. Several recent works have demonstrated that L 1 /L 2 is better than the L 1 norm when approximating the L 0 norm to promote sparsity. Consequently, ...
Sparsity-based nonlinear reconstruction of optical parameters in two-photon photoacoustic computed tomography [0.03%]
基于稀疏性的双光子PACT中的光学参数非线性重建方法
Madhu Gupta,Rohit Kumar Mishra,Souvik Roy
Madhu Gupta
We present a new nonlinear optimization approach for the sparse reconstruction of single-photon absorption and two-photon absorption coefficients in the photoacoustic computed tomography (PACT). This framework comprises of minimizing an obj...
Elena Celledoni,Matthias J Ehrhardt,Christian Etmann et al.
Elena Celledoni et al.
In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of this approach have motivated a line of research into incorpo...
Variational regularisation for inverse problems with imperfect forward operators and general noise models [0.03%]
不完美的正向算子和一般噪声模型下的变分正则化方法研究
Leon Bungert,Martin Burger,Yury Korolev et al.
Leon Bungert et al.
We study variational regularisation methods for inverse problems with imperfect forward operators whose errors can be modelled by order intervals in a partial order of a Banach lattice. We carry out analysis with respect to existence and co...
The influence of numerical error on parameter estimation and uncertainty quantification for advective PDE models [0.03%]
数值误差对_advective_PDE_模型参数估计和不确定性量化的影响
John T Nardini,D M Bortz
John T Nardini
Advective partial differential equations can be used to describe many scientific processes. Two significant sources of error that can cause difficulties in inferring parameters from experimental data on these processes include (i) noise fro...