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

Total Cost €


EC-Contrib. €






 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.

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

Project "SWING" data sheet

The following table provides information about the project.


Organization address
address: PETERSPLATZ 1
city: BASEL
postcode: 4051

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


Take a look of project's partnership.

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


 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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