Search Results for author:
Found 6 papers, 1 papers with code
Multilevel CNNs for Parametric PDEs
We combine concepts from multilevel solvers for partial differential equations (PDEs) with neural network based deep learning and propose a new methodology for the efficient numerical solution of high-dimensional parametric PDEs.
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve noise-free inverse problems to near-perfect accuracy?
Near-Exact Recovery for Sparse-View CT via Data-Driven Methods
This work presents an empirical study on the design and training of iterative neural networks for image reconstruction from tomographic measurements with unknown geometry.
A neural multilevel method for high-dimensional parametric PDEs
In scientific machine learning, neural networks recently have become a popular tool for learning the solutions of differential equations.
Uncertainty Quantification
Vocal Bursts Intensity Prediction
Expressivity of Deep Neural Networks
In this review paper, we give a comprehensive overview of the large variety of approximation results for neural networks.
Error bounds for approximations with deep ReLU neural networks in $W^{s,p}$ norms
We analyze approximation rates of deep ReLU neural networks for Sobolev-regular functions with respect to weaker Sobolev norms.
