IEEE

Limitations of Deep Learning for Inverse Problems on Digital Hardware

Abstract: Deep neural networks have seen tremendous success over the last years. Since the training is performed on digital hardware, in this paper, we analyze what actually can be computed on current ...
IEEE

Score-Based Reverse Mean Propagation for Solving Inverse Problems

Existing diffusion-based methods for inverse problems sample from the posterior using score functions and accept the generated random samples as solutions. In applications that posterior mean is ...
GitHub

Hybrid Regularization Improves Diffusion-based Inverse Problem Solving (ICLR 2025)

This is the README file for the implementation of Hybrid Regularization Improves Diffusion-based Inverse Problem Solving (HRDIS). 🌟Comparison between RED-diff and our proposed HRDIS. HRDIS can ...
GitHub

Finite basis physics-informed neural networks (FBPINNs)

This repository allows you to solve forward and inverse problems related to partial differential equations (PDEs) using finite basis physics-informed neural networks (FBPINNs). To improve the ...