Data Science in Engineering, Volume 10 [electronic resource] : Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023 / edited by Ramin Madarshahian, François Hemez.
Tipo de material:
TextoSeries Conference Proceedings of the Society for Experimental Mechanics SeriesEditor: Cham : Springer Nature Switzerland : Imprint: Springer, 2023Edición: 1st ed. 2023Descripción: VIII, 188 p. 1 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783031349461Tema(s): Engineering -- Data processing | Statics | Civil engineering | Mechanical engineering | Data Engineering | Mechanical Statics and Structures | Civil Engineering | Mechanical EngineeringFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 620.00285 Clasificación LoC:TA345-345.5Recursos en línea: Libro electrónico
En: Springer Nature eBookResumen: Data Science in Engineering, Volume 10: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the tenth volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on: Novel Data-driven Analysis Methods Deep Learning Gaussian Process Analysis Real-time Video-based Analysis Applications to Nonlinear Dynamics and Damage Detection High-rate Structural Monitoring and Prognostics.
| Tipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
|---|---|---|---|---|---|---|---|
| Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | 1 | No para préstamo |
Acceso multiusuario
Data Science in Engineering, Volume 10: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the tenth volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on: Novel Data-driven Analysis Methods Deep Learning Gaussian Process Analysis Real-time Video-based Analysis Applications to Nonlinear Dynamics and Damage Detection High-rate Structural Monitoring and Prognostics.
UABC ; Perpetuidad
