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The SWING project investigates the use of machine learning and signal processing to improve wave scattering computations. It aims to develop efficient algorithms for both forward and inverse scattering problems, with applications in molecular imaging and seismic tomography.
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…
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