Flow pattern transition in pipes using data-driven and physics-informed machine learning (2021)
Fonte: Journal of Fluids Engineering. Unidade: EESC
Assuntos: FLUXO DOS LÍQUIDOS, INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, FENOMENOLOGIA, ENGENHARIA MECÂNICA
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QUINTINO, André Mendes et al. Flow pattern transition in pipes using data-driven and physics-informed machine learning. Journal of Fluids Engineering, v. 143, n. 3, p. 1-11, 2021Tradução . . Disponível em: https://doi.org/10.1115/1.4048876. Acesso em: 06 out. 2024.APA
Quintino, A. M., Rocha, D. L. L. N., Fonseca Júnior, R., & Hernandez Rodriguez, O. M. (2021). Flow pattern transition in pipes using data-driven and physics-informed machine learning. Journal of Fluids Engineering, 143( 3), 1-11. doi:10.1115/1.4048876NLM
Quintino AM, Rocha DLLN, Fonseca Júnior R, Hernandez Rodriguez OM. Flow pattern transition in pipes using data-driven and physics-informed machine learning [Internet]. Journal of Fluids Engineering. 2021 ; 143( 3): 1-11.[citado 2024 out. 06 ] Available from: https://doi.org/10.1115/1.4048876Vancouver
Quintino AM, Rocha DLLN, Fonseca Júnior R, Hernandez Rodriguez OM. Flow pattern transition in pipes using data-driven and physics-informed machine learning [Internet]. Journal of Fluids Engineering. 2021 ; 143( 3): 1-11.[citado 2024 out. 06 ] Available from: https://doi.org/10.1115/1.4048876
