# Machine learning S-wave scattering phase shifts bypassing the radial Schrödinger equation

@article{Romualdi2021MachineLS, title={Machine learning S-wave scattering phase shifts bypassing the radial Schr{\"o}dinger equation}, author={Alessandro Romualdi and Gionni Marchetti}, journal={The European Physical Journal B}, year={2021}, volume={94} }

We present a proof of concept machine learning model resting on a convolutional neural network capable of yielding accurate scattering s-wave phase shifts caused by different three-dimensional spherically symmetric potentials at fixed collision energy thereby bypassing the radial Schrödinger equation. In our work, we discuss how the Hamiltonian can serve as a guiding principle in the construction of a physically-motivated descriptor. The good performance, even in presence of bound states in the…

## One Citation

### Estimating scattering potentials in inverse problems with Volterra series and neural networks

- MathematicsThe European Physical Journal A
- 2022

Inverse problems often occur in nuclear physics, when an unknown potential has to be determined from the measured cross sections, phase shifts or other observables. In this paper, a data-driven…

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