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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.

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

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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