June 5, 2024, 4:44 a.m. | Edoardo Centofanti, Massimiliano Ghiotto, Luca F. Pavarino

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02173v1 Announce Type: cross
Abstract: We construct and compare three operator learning architectures, DeepONet, Fourier Neural Operator, and Wavelet Neural Operator, in order to learn the operator mapping a time-dependent applied current to the transmembrane potential of the Hodgkin- Huxley ionic model. The underlying non-linearity of the Hodgkin-Huxley dynamical system, the stiffness of its solutions, and the threshold dynamics depending on the intensity of the applied current, are some of the challenges to address when exploiting artificial neural networks to …

abstract architectures arxiv construct cs.lg cs.na current deeponet fourier ionic learn mapping math.na potential type wavelet

Senior Data Engineer

@ Displate | Warsaw

Principal Software Engineer

@ Microsoft | Prague, Prague, Czech Republic

Sr. Global Reg. Affairs Manager

@ BASF | Research Triangle Park, NC, US, 27709-3528

Senior Robot Software Developer

@ OTTO Motors by Rockwell Automation | Kitchener, Ontario, Canada

Coop - Technical Service Hub Intern

@ Teradyne | Santiago de Queretaro, MX

Coop - Technical - Service Inside Sales Intern

@ Teradyne | Santiago de Queretaro, MX