SciML for retrieving underlying dynamics from high frequency data
Title: SciML for retrieving underlying dynamics from high frequency data
DNr: NAISS 2024/22-487
Project Type: NAISS Small Compute
Principal Investigator: Jon Norberg <jon.norberg@su.se>
Affiliation: Stockholms universitet
Duration: 2024-04-02 – 2025-05-01
Classification: 10611
Keywords:

Abstract

I have 4 years of hourly data from a in situ plankton camera with AI species detection, i.e. genera level population dynamics of plankton for 4 years. I want to apply SciML methods from the Julia ecosystem to Retreive the underlying dynamics of the system using a neural net training. This first phase is to examine the types of neural-network needed and the sensitivity to simulation parameter choices.