Sebastian Micluța-Câmpeanu
Software Eng. at JuliaHub & PhD student at University of Bucharest.
Sessions
Universal Differential Equations (or UDEs for short) have emerged as a novel way to integrate information
from experimental data into mechanistic models. One of the most important questions when using UDEs
is what equations to modify in order to add the effects of a neural network. In this talk we will
explore what kind of corrections we can make based on the architecture of the UDE.
Knowledge of the physical laws acting on a system is often incomplete. These gaps in our knowledge are referred to as missing physics. Neural network based techniques, post-processed with interpretable machine learning techniques such as symbolic regression, are one way to learn this missing physics. We propose an efficient data gathering technique which aims to make both the fitting and post-processing of the neural network as precise as possible, showcased through a bioreactor case study.