Pablo G. Morato

Room: N3632

Phone: +49 89 289 23049

E-Mail: pablo.morato@tum.de

Office hours: by arrangement

Curriculum Vitae

Since 2025 | Senior Researcher at the Technical University of Munich (TUM)
2023 - 2025 | Postdoctoral Researcher at Delft University of Technology (TU Delft)
2023 | Postdoctoral Researcher at the Technical University of Denmark (DTU)
2021 - 2023 | Postdoctoral Researcher at the University of Liege (ULiege)
2017 - 2021 | PhD in Engineering Sciences, University of Liege (ULiege)
2015 - 2017 | European Master in Advanced Ship Design and Offshore Structures (ULiege, Ecole Centrale de Nantes, University of Rostock, University of Michigan)
2014 - 2015 | M.Sc. in Sustainable Engineering: Offshore Renewable Energy (University of Strathclyde)
2010 - 2014 | B. Eng. in Maritime Engineering (Polytechnic University of Madrid)

Structural reliability, risk analysis, and life-cycle optimizationDecision-making under uncertainty (MDPs, POMDPs, RL, MARL)
Structural reliability, risk analysis, and life-cycle optimization
Probabilistic modeling and Bayesian inference
Inspection, monitoring, and maintenance planning
Uncertainty quantification and active learning (applied to structural reliability and pre-trained foundation models)
Coordination and communication in cooperative multi-agent systems
AI-driven management of large-scale infrastructure systems and smart cities (Offshore) wind energy and renewable energy syste

Hlaing, N., Morato, P. G., Santos, F. D. N., Weijtjens, W., Devriendt, C., & Rigo, P. (2024). Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks. Structural Health Monitoring, 23(3), 1641-1663. https://doi.org/10.1177/14759217231186048
Leroy, P., Morato, P. G., Pisane, J., Kolios, A., & Ernst, D. (2023). IMP-MARL: a suite of environments for large-scale infrastructure management planning via MARL. Advances in Neural Information Processing Systems, 36, 53522-53551. https://openreview.net/forum?id=q3FJk2Nvkk
Morato, P. G., Andriotis, C. P., Papakonstantinou, K. G., & Rigo, P. (2023). Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning. Reliability Engineering & System Safety, 235, 109144. https://doi.org/10.1016/j.ress.2023.109144
Morato, P. G., Papakonstantinou, K. G., Andriotis, C. P., Nielsen, J. S., & Rigo, P. (2022). Optimal inspection and maintenance planning for deteriorating structural components through dynamic Bayesian networks and Markov decision processes. Structural Safety, 94, 102140. https://doi.org/10.1016/j.strusafe.2021.102140