A. Hansen

Publications
Influence

P. Moriarty, A. Hansen
Engineering
2005

AeroDyn is a set of routines used in conjunction with an aeroelastic simulation code to predict the aerodynamics of horizontal axis wind turbines. These subroutines provide several different models… Expand

Breaking the coherence barrier: A new theory for compressed sensing

B. Adcock, A. Hansen, C. Poon, B. Roman
Mathematics, Computer Science
3 February 2013

We introduce a mathematical framework that generalizes the three standard pillars of compressed sensing - namely, sparsity, incoherence and uniform random subsampling - to three new concepts: asymptotic sparsity and multilevel random sampling. Expand

Generalized Sampling and Infinite-Dimensional Compressed Sensing

B. Adcock, A. Hansen
Mathematics, Computer Science
Found. Comput. Math.
1 October 2016

We introduce and analyze a framework and corresponding method for compressed sensing in infinite dimensions. Expand

On the Solvability Complexity Index, the n-pseudospectrum and approximations of spectra of operators

A. Hansen, A. Hansen
Mathematics
2011

We show that it is possible to compute spectra and pseudospectra of linear operators on separable Hilbert spaces given their matrix elements. The core in the theory is pseudospectral analysis and in… Expand

On asymptotic structure in compressed sensing

B. Roman, A. Hansen, B. Adcock
Mathematics, Computer Science
ArXiv
16 June 2014

This paper demonstrates how new principles of compressed sensing, namely asymptotic incoherence, asymmptotic sparsity and multilevel sampling, can be utilised to better understand underlying phenomena in practical compressed sensing. Expand

A Generalized Sampling Theorem for Stable Reconstructions in Arbitrary Bases

B. Adcock, A. Hansen
Mathematics, Computer Science
12 July 2010

We introduce a generalized framework for sampling and reconstruction in separable Hilbert spaces. Expand

On instabilities of deep learning in image reconstruction - Does AI come at a cost?

Vegard Antun, Francesco Renna, C. Poon, B. Adcock, A. Hansen
Computer Science, Medicine
Proceedings of the National Academy of Sciences…
14 February 2019

In this paper, we demonstrate a crucial phenomenon: Deep learning typically yields unstable methods for image reconstruction with potential to change the field. Expand

Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing

B. Adcock, A. Hansen, C. Poon, B. Roman
Computer Science
ArXiv
4 February 2013

We introduce a mathematical framework that bridges a substantial gap between compressed sensing theory and its current use in real-world applications. Expand

Beyond Consistent Reconstructions: Optimality and Sharp Bounds for Generalized Sampling, and Application to the Uniform Resampling Problem

B. Adcock, A. Hansen, C. Poon
Computer Science, Mathematics
SIAM J. Math. Anal.
13 January 2013

Generalized sampling is a recently developed linear framework for sampling and reconstruction in separable Hilbert spaces. Expand

Generalized sampling: stable reconstructions, inverse problems and compressed sensing over the continuum

B. Adcock, A. Hansen, B. Roman, G. Teschke
Mathematics, Computer Science
ArXiv
3 October 2013

We propose a theory and set of methods for infinite-dimensional compressed sensing, or as we shall also refer to it, compressed sensing over the continuum. Expand

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