Výsledky vyhledávání - "Marco Panesi"

Akademický článek

Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows

Autor: Ivan Zanardi, Simone Venturi, Marco Panesi

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-22 (2023)

Abstract This work proposes a new machine learning (ML)-based paradigm aiming to enhance the computational efficiency of non-equilibrium reacting flow simulations while ensuring compliance with the underlying physics. The framework combines dimension

Externí odkaz: https://doaj.org/article/db743d5c00d84f7a837ea810eb2f76c6

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Efficient quasi-classical trajectory calculations by means of neural operator architectures

Autor: Maitreyee Sharma Priyadarshini, Simone Venturi, Ivan Zanardi, Marco Panesi

Publikováno v: Physical Chemistry Chemical Physics. 25:13902-13912

An accurate description of non-equilibrium chemistry relies on rovibrational state-to-state (StS) kinetics data, which can be obtained through the quasi-classical trajectory (QCT) method for high-energy collisions. However, these calculations still r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e5ed7afcf142179eead725ff71ae370
https://doi.org/10.1039/d2cp05506f

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Comprehensive Study of HCN: Potential Energy Surfaces, State-to-State Kinetics, and Master Equation Analysis

Autor: Maitreyee Sharma Priyadarshini, Sung Min Jo, Simone Venturi, David W. Schwenke, Richard L. Jaffe, Marco Panesi

Publikováno v: The Journal of Physical Chemistry A. 126:8249-8265

Understanding the kinetics of the HCN system is critical to several disciplines in science and engineering, including interstellar chemistry, atmospheric reentry, and combustion, to name a few. This paper constructs a rovibrational state-specific kin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc0b5dacbc4c0318d0f267588d244ab9
https://doi.org/10.1021/acs.jpca.2c03959

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