Physics-informed ML models for processing of spectroscopic data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F21%3APU141534" target="_blank" >RIV/00216305:26620/21:PU141534 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26620/21:PU142524

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Physics-informed ML models for processing of spectroscopic data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Physics-informed ML models for processing of spectroscopic data

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF19_073%2F0016948" target="_blank" >EF19_073/0016948: Kvalitní interní granty VUT</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů