Publication detail

Physics-informed ML models for processing of spectroscopic data

VRÁBEL, J.

Original Title

Physics-informed ML models for processing of spectroscopic data

Type

abstract

Language

English

Original Abstract

Massive adoption of machine learning (ML) techniques in spectroscopy brought entirely new possibilities in analytical performance for applications, and also for basic research. However, several problems emerged, e.g. ML models are often utilized as “black-boxes”, or considerably overtrained. Another issue is a blind transition of successful models (architecture, parameters) from distinct applications (e.g. image processing) to spectroscopic tasks, without taking into account the properties of data. We study the influence of (spectroscopic) data properties and incorporate them into ML models in form of weight initializations, specific parameter penalizations, and invariances. This leads to an increased analytical performance of models and better interpretability

Keywords

machine learning, spectroscopic data, interpretability, physics-informed, inductive bias

Authors

VRÁBEL, J.

Released

24. 8. 2021

BibTex

@misc{BUT175293,
  author="Jakub {Vrábel}",
  title="Physics-informed ML models for processing of spectroscopic data",
  year="2021",
  note="abstract"
}