Cover Image

Neural Networks for Bayesian Inverse Problems Governed by a Nonlinear ODE

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Villalobos, German, Rudi, Johann, Mang, Andreas
Format:	Preprint
Published:	2025
Subjects:	Numerical Analysis 68T07, 62F15, 34A55
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Reliable model selection in the presence of parameter non-identifiability
by: Foo, Yong See, et al.
Published: (2026)

Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
by: Wang, Yueyang, et al.
Published: (2026)

Quantifying structural uncertainty in chemical reaction network inference
by: Foo, Yong See, et al.
Published: (2025)

How Regularization Terms Make Invertible Neural Networks Bayesian Point Estimators
by: Heilenkötter, Nick
Published: (2025)

An Unconditional Representation of the Conditional Score in Infinite-Dimensional Linear Inverse Problems
by: Schneider, Fabian, et al.
Published: (2024)

Amortized Energy-Based Bayesian Inference
by: Kaveh, Hojjat, et al.
Published: (2026)

DyKAF: Dynamical Kronecker Approximation of the Fisher Information Matrix for Gradient Preconditioning
by: Yudin, Nikolay, et al.
Published: (2025)

A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization
by: Hart, Emma, et al.
Published: (2025)

Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography
by: Sahlström, Teemu, et al.
Published: (2022)

Bayesian inference of covariate-parameter relationships for population modelling
by: Lie, Han Cheng
Published: (2024)

Low-rank Bayesian matrix completion via geodesic Hamiltonian Monte Carlo on Stiefel manifolds
by: Cui, Tiangang, et al.
Published: (2024)

A new sparsity promoting residual transform operator for Lasso regression
by: Xiao, Yao, et al.
Published: (2025)

Solving Nonlinear Energy Supply and Demand System Using Physics-Informed Neural Networks
by: Vo, Van Truong, et al.
Published: (2024)

Neural Approximate Inverse Preconditioners
by: Xu, Tianshi, et al.
Published: (2025)

Diffusion Graph Posterior Sampling for Nonlinear Inverse Problems with Application to Electrical Impedance Tomography
by: Alberti, Giovanni S., et al.
Published: (2026)

MCMC-Net: Accelerating Markov Chain Monte Carlo with Neural Networks for Inverse Problems
by: Majee, Sudeb, et al.
Published: (2024)

Tensor cross interpolation for global discrete optimization with application to Bayesian network inference
by: Dolgov, Sergey, et al.
Published: (2025)

Posterior Bayesian Neural Networks with Dependent Weights
by: Apollonio, Nicola, et al.
Published: (2025)

Projections for handling uncertainties and enabling domain truncation in diffuse optical tomography
by: Hakula, Aada, et al.
Published: (2026)

Designing experimental conditions to use the Lotka-Volterra model to infer tumor cell line interaction types
by: Cho, Heyrim, et al.
Published: (2022)

Learning a generalized multiscale prolongation operator
by: Liu, Yucheng, et al.
Published: (2024)

Sensitivity Analysis of the Information Gain in Infinite-Dimensional Bayesian Linear Inverse Problems
by: Chowdhary, Abhijit, et al.
Published: (2023)

Universal Approximation of Dynamical Systems by Semi-Autonomous Neural ODEs and Applications
by: Li, Ziqian, et al.
Published: (2024)

Neural-Guided Domain Restriction to Accelerate Pseudospectra Computation for Structured Non-normal Banded Matrices
by: Punia, Amit, et al.
Published: (2026)

Randomized algorithms for streaming low-rank approximation in tree tensor network format
by: Bucci, Alberto, et al.
Published: (2024)

A sequential multilinear Nyström algorithm for streaming low-rank approximation of tensors in Tucker format
by: Bucci, Alberto, et al.
Published: (2024)

Fast and Accurate Interpolative Decompositions for General, Sparse, and Structured Tensors
by: Zhang, Yifan, et al.
Published: (2025)

Low-rank approximation of parameter-dependent matrices via CUR decomposition
by: Park, Taejun, et al.
Published: (2024)

Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators Under Mesh Refinement
by: Sanz-Alonso, Daniel, et al.
Published: (2023)

Randomized algorithms for computing the tensor train approximation and their applications
by: Che, Maolin, et al.
Published: (2024)

Efficient randomized algorithms for the fixed Tucker-rank problem of Tucker decomposition with adaptive shifts
by: Che, Maolin, et al.
Published: (2025)

Optimal Sensor Placement in Gaussian Processes via Column Subset Selection
by: Chen, Jessie, et al.
Published: (2026)

Fast Rank Adaptive CUR via a Recycled Small Sketch
by: Pritchard, Nathaniel, et al.
Published: (2025)

Beyond singular value gaps in randomized subspace approximation
by: Wang, Christopher, et al.
Published: (2026)

Density-Matrix Spectral Embeddings for Categorical Data: Operator Structure and Stability
by: Bosch-Romeu, Raquel, et al.
Published: (2026)

A Variational Physics-Informed Neural Network Framework Using Petrov-Galerkin Method for Solving Singularly Perturbed Boundary Value Problems
by: Kumar, Vijay, et al.
Published: (2025)

Nonlinear filtering based on density approximation and deep BSDE prediction
by: Bågmark, Kasper, et al.
Published: (2025)

Bayesian Jammer Localization with a Hybrid CNN and Path-Loss Mixture of Experts
by: Jaramillo-Civill, Mariona, et al.
Published: (2025)

Application of machine learning regression models to inverse eigenvalue problems
by: Pallikarakis, Nikolaos, et al.
Published: (2022)

High-dimensional Bayesian filtering through deep density approximation
by: Bågmark, Kasper, et al.
Published: (2025)