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Artigo de periodico
Assessing the potential of machine learning for predicting the nonlinear refractive index of glasses (2025)
Saraiva, Murilo Neco; Mendonça, Cleber Renato
Source: Journal of Applied Physics. Unidade: IFSC
Subjects: VIDRO, ÓPTICA NÃO LINEAR, INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SARAIVA, Murilo Neco e MENDONÇA, Cleber Renato. Assessing the potential of machine learning for predicting the nonlinear refractive index of glasses. Journal of Applied Physics, v. 138, n. 14, p. 143103-1-143103-12 + supplementary material, 2025Tradução . . Disponível em: https://doi.org/10.1063/5.0289970. Acesso em: 09 nov. 2025.
APA
Saraiva, M. N., & Mendonça, C. R. (2025). Assessing the potential of machine learning for predicting the nonlinear refractive index of glasses. Journal of Applied Physics, 138( 14), 143103-1-143103-12 + supplementary material. doi:10.1063/5.0289970
NLM
Saraiva MN, Mendonça CR. Assessing the potential of machine learning for predicting the nonlinear refractive index of glasses [Internet]. Journal of Applied Physics. 2025 ; 138( 14): 143103-1-143103-12 + supplementary material.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1063/5.0289970
Vancouver
Saraiva MN, Mendonça CR. Assessing the potential of machine learning for predicting the nonlinear refractive index of glasses [Internet]. Journal of Applied Physics. 2025 ; 138( 14): 143103-1-143103-12 + supplementary material.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1063/5.0289970
Artigo de periodico
Driven electron g-factor anisotropy in layered III–V semiconductors: interfacing, tunnel coupling, and structure inversion asymmetry effects (2024)
Sandoval, Marcelo Alejandro Toloza ; Padilla, Jhon Elber Leon ; Wanderley, Adilson Barros ; Sipahi, Guilherme Matos ; Chubaci, José Fernando Diniz ; Silva, Antonio Ferreira da
Source: Journal of Applied Physics. Unidades: IFSC, IF
Subjects: APRENDIZADO COMPUTACIONAL, SEMICONDUTORES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANDOVAL, Marcelo Alejandro Toloza et al. Driven electron g-factor anisotropy in layered III–V semiconductors: interfacing, tunnel coupling, and structure inversion asymmetry effects. Journal of Applied Physics, v. 135, n. 10, p. 103901-1-103901-9, 2024Tradução . . Disponível em: https://doi.org/10.1063/5.0187962. Acesso em: 09 nov. 2025.
APA
Sandoval, M. A. T., Padilla, J. E. L., Wanderley, A. B., Sipahi, G. M., Chubaci, J. F. D., & Silva, A. F. da. (2024). Driven electron g-factor anisotropy in layered III–V semiconductors: interfacing, tunnel coupling, and structure inversion asymmetry effects. Journal of Applied Physics, 135( 10), 103901-1-103901-9. doi:10.1063/5.0187962
NLM
Sandoval MAT, Padilla JEL, Wanderley AB, Sipahi GM, Chubaci JFD, Silva AF da. Driven electron g-factor anisotropy in layered III–V semiconductors: interfacing, tunnel coupling, and structure inversion asymmetry effects [Internet]. Journal of Applied Physics. 2024 ; 135( 10): 103901-1-103901-9.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1063/5.0187962
Vancouver
Sandoval MAT, Padilla JEL, Wanderley AB, Sipahi GM, Chubaci JFD, Silva AF da. Driven electron g-factor anisotropy in layered III–V semiconductors: interfacing, tunnel coupling, and structure inversion asymmetry effects [Internet]. Journal of Applied Physics. 2024 ; 135( 10): 103901-1-103901-9.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1063/5.0187962
Artigo de periodico
Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework (2020)
Santos, Denise P ; Pelissari, Pedro Ivo Batistel Galiote Brossi ; Mello, Rodrigo Fernandes de ; Pandolfelli, Victor Carlos
Source: Journal of Applied Physics. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, MÉTODO DOS ELEMENTOS FINITOS, FORNO ELÉTRICO, TRANSFERÊNCIA DE CALOR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Denise P et al. Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework. Journal of Applied Physics, v. 127, n. 21, p. 215104-1-215104-7, 2020Tradução . . Disponível em: https://doi.org/10.1063/5.0004395. Acesso em: 09 nov. 2025.
APA
Santos, D. P., Pelissari, P. I. B. G. B., Mello, R. F. de, & Pandolfelli, V. C. (2020). Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework. Journal of Applied Physics, 127( 21), 215104-1-215104-7. doi:10.1063/5.0004395
NLM
Santos DP, Pelissari PIBGB, Mello RF de, Pandolfelli VC. Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework [Internet]. Journal of Applied Physics. 2020 ; 127( 21): 215104-1-215104-7.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1063/5.0004395
Vancouver
Santos DP, Pelissari PIBGB, Mello RF de, Pandolfelli VC. Estimating the thermal insulating performance of multi-component refractory ceramic systems based on a machine learning surrogate model framework [Internet]. Journal of Applied Physics. 2020 ; 127( 21): 215104-1-215104-7.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1063/5.0004395

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