Data-Driven Modeling of Photosynthesis Regulation Under Oscillating Light Condition - Part I: In-Silico Exploration
Abstract
This paper explores the application of data-driven system identification techniques in the frequency domain to obtain simplified, control-oriented models of photosynthesis regulation under oscillating light conditions. In-silico datasets are generated using simulations of the physics-based Basic DREAM Model (BDM) Funete et al.[2024], with light intensity signals -- comprising DC (static) and AC (modulated) components as input and chlorophyll fluorescence (ChlF) as output. Using these data, the Best Linear Approximation (BLA) method is employed to estimate second-order linear time-invariant (LTI) transfer function models across different operating conditions defined by DC levels and modulation frequencies of light intensity. Building on these local models, a Linear Parameter-Varying (LPV) representation is constructed, in which the scheduling parameter is defined by the DC values of the light intensity, providing a compact state-space representation of the system dynamics.
Keywords
Cite
@article{arxiv.2511.04330,
title = {Data-Driven Modeling of Photosynthesis Regulation Under Oscillating Light Condition - Part I: In-Silico Exploration},
author = {Christian Portilla and Arviandy G Aribowo and Ramachandran Anantharaman and César A Gómez-Pérez and Leyla Özkan},
journal= {arXiv preprint arXiv:2511.04330},
year = {2025}
}
Comments
10 pages, 14 figures