Machine learning to process data from innovative sensor for gas-liquid flow characterization in millimeter-scale channels

Authors

Hélène Chaumat (ORCID: https://orcid.org/0000-0002-0360-1464)
Anne‐Marie Billet (ORCID: https://orcid.org/0000-0002-2589-638X)
Rachid Ouaret (ORCID: https://orcid.org/0000-0002-0502-2108)
Audrey Devatine

Institutions

Laboratoire de Génie Chimique (FR)

Publication Details

Journal: SPIRE - Sciences Po Institutional REpository
Published: 2024-09-02
Primary Topic: Advanced Chemical Sensor Technologies
Type: article
Field-Weighted Citation Impact: 0.00

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article

Machine learning to process data from innovative sensor for gas-liquid flow characterization in millimeter-scale channels

Hélène Chaumat, Anne‐Marie Billet, Rachid Ouaret, Audrey Devatine

Sep 2, 2024

SPIRE - Sciences Po Institutional REpository

Advanced Chemical Sensor Technologies

article

Machine learning to process data from innovative sensor for gas-liquid flow characterization in millimeter-scale channels

Hélène Chaumat, Anne‐Marie Billet, Rachid Ouaret, Audrey Devatine

September 2, 2024article en

Abstract

No abstract available for this paper.

SPIRE - Sciences Po Institutional REpository

Laboratoire de Génie Chimique (FR)

Affordable and clean energy

Openalex Percentile: Top 82%

Advanced Chemical Sensor Technologies

OpenAlex

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Machine learning to process data from innovative sensor for gas-liquid flow characterization in millimeter-scale channels — Hélène Chaumat, Anne‐Marie Billet, et al. · SPIRE - Sciences Po Institutional REpository (2024) | TGRS Research Map | TGRS