Pablo G. Morato

TUM's Engineering Risk Analysis Group

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School of Engineering and Design, Technical University of Munich

Theresienstr. 90

80333 Munich, Germany

I am a Senior Researcher in engineering risk analysis and artificial intelligence, @ ERA, TUM.

My research focuses on the development of probabilistic and data-driven decision-making methods for engineering systems operating under uncertainty. I work at the intersection of engineering sciences and artificial intelligence, with an emphasis on sequential decision-making, risk-aware optimization, and life-cycle management of infrastructure systems.

A central theme of my work is the use of Markov decision processes, partially observable decision models, and reinforcement learning to design adaptive inspection, monitoring, and maintenance strategies. I combine these methods with Bayesian inference, uncertainty quantification, and reliability analysis to enable informed decisions in settings characterized by partial observability, model uncertainty, and long time horizons.

My research is motivated by applications in large-scale and networked engineering systems, particularly in offshore wind energy and critical infrastructure, where scalability, interpretability, and robustness are essential. More broadly, I am interested in bridging classical reliability theory and modern AI methods, and in developing computational tools that translate probabilistic models into practical, risk-aware decision support.

news

Dec 2025 Co-organizing the MS139 Optimization under Uncertainty at WCCM-ECCOMAS 2026. 19 to 24 July 2026 @ Munich, Germany.
Dec 2025 Joined the Engineering Risk Analysis Group at TUM as a Senior Researcher.
Dec 2023 Presenting IMP-MARL at NeurIPS2023.
Sep 2023 Joined the AiDAPT Lab at TU Delft as a Postdoctoral Researcher.
Jun 2023 IMP-MARL released.

selected publications

2024

  1. Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks
    Nandar Hlaing, Pablo G Morato, Francisco de Nolasco Santos, and 3 more authors
    Structural Health Monitoring, 2024

2023

  1. Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning
    Pablo G Morato, Charalampos P Andriotis, Konstantinos G Papakonstantinou, and 1 more author
    Reliability Engineering & System Safety, 2023
  2. IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL
    Pascal Leroy, Pablo G Morato, Jonathan Pisane, and 2 more authors
    arXiv preprint arXiv:2306.11551, 2023
  3. Interpretation and analysis of deep reinforcement learning driven inspection and maintenance policies for engineering systems
    Pablo G Morato, Konstantinos G Papakonstantinou, Charalampos P Andriotis, and 2 more authors
    In 14th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP14), 2023

2022

  1. Optimal inspection and maintenance planning for deteriorating structural components through dynamic Bayesian networks and Markov decision processes
    Pablo G Morato, Konstantinos G Papakonstantinou, Charalampos P Andriotis, and 2 more authors
    Structural Safety, 2022
  2. Managing offshore wind turbines through Markov decision processes and dynamic Bayesian networks
    PG Morato, KG Papakonstantinou, C Andriotis, and 1 more author
    In 13th International Conference on Structural Safety & Reliability (ICOSSAR), Shanghai, China, 2022