Source: Proceedings. Conference titles: Ibero-Latin-American Congress on Computational Methods in Engineering - CILAMCE. Unidade: EP
Subjects: REDES NEURAIS, INTELIGÊNCIA ARTIFICIAL, MATERIAIS GRANULARES
ABNT
QUINTANA RUIZ, Osvaldo Dario e CAMPELLO, Eduardo de Morais Barreto. A methodology to predict the effective thermal conductivity of a granular assembly using deep learning. 2022, Anais.. Rio de Janeiro: ABMEC, 2022. Disponível em: https://repositorio.usp.br/directbitstream/525e5a7e-f7ce-4e15-a41d-c3f6c2b29fd7/A_methodology_to_predict_the_effective_thermal_conductivity_of_a_granular_assembly_using_deep_learning.pdf. Acesso em: 19 nov. 2024.APA
Quintana Ruiz, O. D., & Campello, E. de M. B. (2022). A methodology to predict the effective thermal conductivity of a granular assembly using deep learning. In Proceedings. Rio de Janeiro: ABMEC. Recuperado de https://repositorio.usp.br/directbitstream/525e5a7e-f7ce-4e15-a41d-c3f6c2b29fd7/A_methodology_to_predict_the_effective_thermal_conductivity_of_a_granular_assembly_using_deep_learning.pdfNLM
Quintana Ruiz OD, Campello E de MB. A methodology to predict the effective thermal conductivity of a granular assembly using deep learning [Internet]. Proceedings. 2022 ;[citado 2024 nov. 19 ] Available from: https://repositorio.usp.br/directbitstream/525e5a7e-f7ce-4e15-a41d-c3f6c2b29fd7/A_methodology_to_predict_the_effective_thermal_conductivity_of_a_granular_assembly_using_deep_learning.pdfVancouver
Quintana Ruiz OD, Campello E de MB. A methodology to predict the effective thermal conductivity of a granular assembly using deep learning [Internet]. Proceedings. 2022 ;[citado 2024 nov. 19 ] Available from: https://repositorio.usp.br/directbitstream/525e5a7e-f7ce-4e15-a41d-c3f6c2b29fd7/A_methodology_to_predict_the_effective_thermal_conductivity_of_a_granular_assembly_using_deep_learning.pdf