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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.

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

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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