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- W4386301905 abstract "Recent advances in machine learning establish the ability of certain neural-network architectures called neural operators to approximate maps between function spaces. Motivated by a prospect of employing them in fundamental physics, we examine applications to scattering processes in quantum mechanics. We use an iterated variant of Fourier neural operators to learn the physics of Schrodinger operators, which map from the space of initial wave functions and potentials to the final wave functions. These deep operator learning ideas are put to test in two concrete problems: a neural operator predicting the time evolution of a wave packet scattering off a central potential in $1+1$ dimensions, and the double-slit experiment in $2+1$ dimensions. At inference, neural operators can become orders of magnitude more efficient compared to traditional finite-difference solvers." @default.
- W4386301905 created "2023-08-31" @default.
- W4386301905 creator A5076831221 @default.
- W4386301905 date "2023-08-28" @default.
- W4386301905 modified "2023-09-27" @default.
- W4386301905 title "Scattering with Neural Operators" @default.
- W4386301905 doi "https://doi.org/10.48550/arxiv.2308.14789" @default.
- W4386301905 hasPublicationYear "2023" @default.
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