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Found 6 papers, 1 papers with code
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?
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.
Expressivity of Deep Neural Networks
In this review paper, we give a comprehensive overview of the large variety of approximation results for neural networks.
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
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.
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.
