- Ph.D., University of Liverpool, UK, 2019
- MRes (Hons), University of Liverpool, UK, 2015
- M.Eng., Bologna University, IT, 2014
- B.Eng., Bologna University, IT, 2012
- Research Scholar, NIA, 2019-present
- Research internship, ARAMIS s.r.l, 2017-2018
- Probabilistic and non-probabilistic methods for uncertainty quantification
- Networked infrastructure modelling, simulation, design optimization
- Resilience, Reliability, Vulnerability and Risk assessments
- Reinforcement learning methods for optimization of operations and maintenances
- Bayesian model updating and surrogate modeling
Developing a mathematical framework for optimizing systems under uncertainty
NASA missions often require innovative new systems which are designed specifically to operate in extreme environments under strongly varying operating conditions. Uncertainty is typically described as aleatory uncertainty, which is uncertainty due to randomness, or epistemic uncertainty, which is uncertainty due to knowledge. In many cases, experimental data is extremely costly and therefore highly limited, making uncertainty difficult to definitively quantify. This research instead intends to develop a mathematical framework for determining the effects of uncertainty in the predictions of computational models.
Dependency models to study the effects of radiation exposure on pilots
Since NASA established the Journey to Mars, an initiative for manned missions to Mars, increased emphasis has been placed on research to determine the impact of radiation exposure on astronauts in outer space. Once humans leave the Earth’s protective magnetosphere, they are subject to galactic cosmic rays (GCRs) and solar particle events (SPEs, also known as solar flare particles), not to mention radiation belt particles from the Van Allen Radiation Belts. This research intends to conduct dependency modeling to support studies into the effects of those types of radiation on the human body.
Rocchetta and Y.F. Li and E. Zio., “Risk Assessment and Risk-Cost Optimization of Distributed Generation Systems Considering Extreme Weather Conditions”, Reliability Engineering and System Safety, Elsevier, Volume 136, pp 47 – 61, 2015
Rocchetta, E. Patelli and E. Zio, “A Power-Flow Emulator Approach for Resilience Assessment of Repairable Power Grids subject to Weather-Induced Failures and Data Deﬁciency”, Applied Energy, Elsevier, Volume 210, 15, pp 339-350, 2018
Rocchetta and E. Patelli, “Assessment of Power Grid Vulnerabilities Accounting for Stochastic Loads and Model Imprecision”, International Journal for Electrical Power & Energy Systems, Volume 98, pp 219-232, 2018
Rocchetta, E. Patelli, M. Broggi and Q. Huchet, “On-Line Bayesian Model Updating for Structural Health Monitoring”, Mechanical Systems and Signal Processing, Volume 103, 174 – 195, 2018,
Rocchetta, E. Patelli and M. Broggi, “Do we have enough data? Robust reliability via uncertainty quantification”, Applied Mathematical Modelling, Volume 54, pp 710-721, 2018.
Rocchetta, L. Bing, E. Patelli and G. Sansavini, “Effect of Load-Generation Variability on Power Grid Cascading Failures”, ESREL 2018 conference, Trondheim, Norway, June 2018.
Rocchetta, M. Compare, E. Patelli, L. Bellani and E. Zio, “A Reinforcement Learning Framework for Optimisation of Power Grid Operations and Maintenance”, 8th international workshop on reliable engineering computing, REC 2018, Liverpool, UK, Jully, 2018.
Rocchetta and E. Patelli, “Stochastic Analysis and Reliability-Cost Optimization of Distributed Generators and Air Source Heat Pumps”, to be presented at the 2nd International Conference on System Reliability and Safety, ICSRS, 2017.
Rocchetta and E. Patelli, “An Efficient Framework for Reliability Assessment of Power Networks Installing Renewable Generators and Subject to Parametric P-box Uncertainty”, Proceedings of the 27th European Safety and Reliability Conference (ESREL 2017), Portoroz, Slovenia, pp. 3253 – 3260.
Rocchetta and E. Patelli, “Power Grid Robustness to Severe Failures: Topological and Flow Based Metrics Comparison”, European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS, Crete 2016, pp. 6121-6135.
Rocchetta, E. Patelli, M. Broggi and Q. Huchet, “On Bayesian Approaches for Real-Time Crack Detection”, Safety and Reliability of Complex Engineered Systems, ESREL, Zurich 2015, pp 1929-1936.