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SWING SIGNED

Signals, Waves, and Learning: A Data-Driven Paradigm for Wave-Based Inverse Problems

Total Cost €

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EC-Contrib. €

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Partnership

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 SWING project word cloud

Explore the words cloud of the SWING project. It provides you a very rough idea of what is the project "SWING" about.

computations    frontier    fine    examine    theory    modalities    roles    operators    wave    playing    fitting    mars    seismic    implementations    unclear    regularization    fourier    guarantees    sparse    discretizations    unlike    tuning    algorithms    underlying    models    rely    wavefields    believe    integral    molecules    computational    emphasis    treat    molecular    giving    indisputable    upcoming    central    spurred    minimal    science    concert    waves    sensing    urgency    derive    techniques    combine    sampling    question    inverse    leveraging    questions    geometry    hall    connections    imaging    efficient    representations    deep    scattering    governs    tomography    explore    modeling    physics    machine    classes    thrusts    successes    theoretic    sharp    unlabeled    learning    structures    popular    approximation    complexity    paradigm    shifted    nonlinearity    breakthroughs    fundamental    signal    data    earth    power    designs    empirical    swing    model    stability    limits    quantify    acoustics    practical   

Project "SWING" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITAT BASEL 

Organization address
address: PETERSPLATZ 1
city: BASEL
postcode: 4051
website: www.unibas.ch

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Total cost 1˙986˙430 €
 EC max contribution 1˙986˙430 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2019-STG
 Funding Scheme ERC-STG
 Starting year 2020
 Duration (year-month-day) from 2020-01-01   to  2024-12-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITAT BASEL CH (BASEL) coordinator 1˙986˙430.00

Map

 Project objective

Scattering of waves governs fundamental questions in science, from imaging molecules to fine-tuning concert hall acoustics. Efficient scattering computations rely on sparse representations of wavefields. Spurred by the empirical successes of deep learning, the emphasis has recently shifted to data-driven modeling. However, unlike signal-theoretic implementations that come with sharp approximation guarantees, it remains unclear whether the popular deep learning structures can represent important scattering operators.

In SWING, we address this question by leveraging advances in signal processing and machine learning. We propose theory and algorithms for the upcoming, learning-based wave of breakthroughs in forward and inverse scattering. SWING is built on three research thrusts: 1. To design efficient computational structures with approximation guarantees for learning scattering operators. We will focus on minimal structures for Fourier integral operators which model key problems. 2. To treat learning for inverse scattering as a sampling problem and derive practical sample complexity results. We will explore connections between learning theory and stability of inverse problems, and examine the regularization roles of data, physics and nonlinearity. 3. To apply our techniques to two classes of inverse problems: (i) emerging modalities in molecular imaging, giving rise to problems in geometry and unlabeled sampling; and (ii) seismic tomography of Earth and Mars, with data-driven discretizations of scattering operators playing a central role. With the growth of wave-based sensing, there is an urgency to quantify the limits of the data-driven paradigm in scattering problems. The power of data in fitting models is indisputable: it is certainly the next frontier. We believe, however, that the best designs combine data-based models with an understanding of the underlying physics.

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The information about "SWING" are provided by the European Opendata Portal: CORDIS opendata.

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