Machine Learning for health monitoring in dams (PI: Fernando Salazar)
Development of predictive models based on machine learning and monitoring data for early detection of anomalies and interpretation of dam behavior.
Numerical modelling of granular materials with DEM. (PI: Joaquin Irazábal)
Development and application of an implementation of the Discrete Element Model for the analysis of granular geomaterials, with emphasis in railway ballast.
Development of advanced numerical models of water distribution systems. Water demand prediction and leakage management (including estimation, detection and isolation) by combining statistical analysis, machine learning and numerical modelling.
Numerical modelling of hydraulic structures. (PI: Javier San Mauro)
Hydraulic analysis of complex, 3D phenomena such as shockwaves or aeration in spillways and bottom outlets. Non-conventional spillways involving fluid-structure interaction. Development of tailored GUIs
Development of hybrid methodologies, combining machine learning and numerical methods to account for uncertainty in complex settings: non-linear, transient and high dimension problems.