{"id":5413,"date":"2024-02-27T18:43:56","date_gmt":"2024-02-27T18:43:56","guid":{"rendered":"http:\/\/lasartemplate.local\/?page_id=5413"},"modified":"2026-04-10T08:26:55","modified_gmt":"2026-04-10T08:26:55","slug":"msc-thesis","status":"publish","type":"page","link":"https:\/\/www.lasar.polimi.it\/?page_id=5413","title":{"rendered":"MSc Theses"},"content":{"rendered":"\n

Theses with international collaborations:<\/em><\/strong><\/p>\n\n\n\n

Theses on risk and resilience assessment of complex systems and critical infrastructures:<\/em><\/strong><\/p>\n\n\n\n

Dynamic RUL Estimates Based on Time Transformation Methods (with SUPSI, Switzerland and Lule\u00e5 University of Technology, Sweden)<\/a><\/a><\/p>\n\n\n\n

Development and Uncertainty Quantification of Explainable AI\/ML Models for Critical Heat Flux Prediction in Water\u0002Cooled Nuclear Reactors (with CEA Paris)<\/a><\/p>\n\n\n\n

Enhancing Resilience in Net Zero Energy Systems (with University of Sheffield)<\/a><\/p>\n\n\n\n

Enhancing Safety in Nuclear-Powered Green Hydrogen Production (with University of Sheffield)<\/a><\/p>\n\n\n\n

Hydrogen Safety in Transportation \u2013 Focusing on Hydrogen-Fueled Ships (with University of Sheffield)<\/a><\/p>\n\n\n\n

Guided probabilistic risk assessment of complex systems using reinforcement learning optimization (with UCLA, USA)<\/a><\/p>\n\n\n\n

Resilience assessment and management of power grids in response to extreme weather conditions (heat wave, storm, flood, wildfire, etc.) (with UCLA, USA)<\/a><\/p>\n\n\n\n

Development of a neural network for Critical Heat Flux predictions (with CEA, France)<\/a><\/p>\n\n\n\n

Real-Time Condition-Informed Safety Assessment of a Microreactor (with MIT, USA)<\/a><\/p>\n\n\n\n

Inverse Uncertainty Quantification with Deep Learning Surrogate Models of Accident Scenarios in Nuclear Microreactors (with MIT, USA)<\/a><\/p>\n\n\n\n

Development of a Digital Twin of a Nuclear Microreactor: on the Multi-fidelity
Uncertainty Quantification (with MIT, USA)<\/a><\/p>\n\n\n\n

Advanced methods of dynamic risk assessment for energy systems (with Leibniz University of Hannover, Germany)<\/a><\/p>\n\n\n\n

Safety analysis of nuclear power plants: quantification of the uncertainty in nuclear thermal-hydraulic codes based on heterogeneous experimental data (ATRIUM Consortium) (with CEA, France)<\/a><\/p>\n\n\n\n

Data adequacy assessment in thermal-hydraulic experimental tests of Intermediate Break Loss of Coolant Accident (IBLOCA) (ATRIUM Consortium)<\/a><\/p>\n\n\n\n

Inverse uncertainty quantification of nuclear thermal-hydraulic codes for safety analysis of Integral Effect Tests nuclear power facilities (ATRIUM Consortium) (in collaboration with ENEA and Politecnico di Torino)<\/a><\/p>\n\n\n\n

Advanced uncertainty quantification models for the prediction of critical\/safety parameters in nuclear power systems by artificial intelligence (EGMUP Task force on AI\/ML)<\/p>\n\n\n\n

Interpretability and explainability of artificial intelligence models for the prediction of the critical\/safety parameters in nuclear power systems (EGMUP Task force on AI\/ML)<\/p>\n\n\n\n

Theses on Prognostics and Health Management for Predictive Maintenance:<\/em><\/strong><\/p>\n\n\n\n

Development of Degradation Assessment Methods of Insulated-Gate Bipolar Transistors (IGBTs) using Generative Artificial Intelligence (GenAI) (SAFEPOWER)<\/a><\/p>\n\n\n\n

Development of State-of-Health Indicators for Researchable Batteries Using Deep-Transfer Learning Methods (with Ecole Polytechnique, France)<\/a><\/p>\n\n\n\n

Deep Transfer Learning Methods for Prognostics and Health Management (PHM) of Batteries (with Tsinghua University, China)<\/a><\/p>\n\n\n\n

Cross-Modal Temporal-Spatial Synchronization for Holistic Fault Evolution Analysis in Industrial PHM Applications (Beihang University, China)<\/a><\/p>\n\n\n\n

Dual-Layer Collaborative Architecture for Knowledge and Physics Integration in PHM Large Models (Beihang University, China)<\/a><\/p>\n\n\n\n

Intelligent Agent-Based Optimization for Multimodal PHM Model Training Under Uncertainty (Beihang University, China)<\/a><\/p>\n\n\n\n

<\/div>\n\n\n\n

Theses with industrial partners<\/em> (with possibility of stage)<\/strong><\/p>\n\n\n\n

Theses on risk and resilience assessment of complex systems and critical infrastructures:<\/em><\/strong><\/p>\n\n\n\n

Benchmark of dynamic methods for Probabilistic Safety Assessment of nuclear power plants (EDF, Paris)<\/a><\/p>\n\n\n\n

Dynamic Probabilistic Safety Assessment (PSA) of Small Modular Reactors (SMRs): Reduced Order Modeling and Advanced Sampling (TRACTEBEL, Belgium)<\/a><\/p>\n\n\n\n

System-Theoretic Accident Model and Processes (STAMP) for Probabilistic Safety Assessment (PSA) in Gen IV Reactor Technologies (TRACTEBEL, Belgium)<\/a><\/p>\n\n\n\n

Automated System Logic Representation by P&I Digitalization (TRACTEBEL, Belgium)<\/a><\/p>\n\n\n\n

Theses on reliability assessment:<\/em><\/strong><\/p>\n\n\n\n

Functional Reliability of Passive Safety Systems of Nuclear Power Plants (NPPs) Reduced Order Modeling and Advanced Sampling (TRACTEBEL, Belgium)<\/a><\/p>\n\n\n\n

Theses on Prognostics and Health Management for Predictive Maintenance:<\/em><\/strong><\/p>\n\n\n\n

Caratterizzazione affidabilistica di componenti industriali (Pietro Fiorentini SpA)<\/a><\/p>\n\n\n\n

Development of machine learning methods based on sensor data for maintenance optimization and failure analysis (Fluid-o-Tech)<\/a><\/p>\n\n\n\n

Development of Physics-informed Deep Learning methods for Remaining Useful Life prediction of Metal-Oxide Semiconductor Field-Effect Transistors (SAFEPOWER European project)<\/a><\/p>\n\n\n\n

Development of a framewok for causal modeling (Aramix)<\/a><\/p>\n\n\n\n

Other:<\/em><\/strong><\/p>\n\n\n\n

Deep reinforcement learning for the optimization of loading plans for NPPs (TRACTEBEL, Belgium)<\/a><\/p>\n\n\n\n

Development of Explainable Multiphysics-Informed Machine Learning for the Prediction of Critical Parameters in Water-cooled Nuclear Reactors (TRACTEBEL, Belgium)<\/a><\/p>\n\n\n\n

Development of a Bayesian Optimization of Experimental Design (BOED) framework (Aramix)<\/a><\/p>\n\n\n\n

<\/div>\n\n\n\n

Theses internal at LASAR:<\/em><\/strong><\/p>\n\n\n\n

Theses on risk and resilience assessment of complex systems and critical infrastructures:<\/em><\/strong><\/p>\n\n\n\n

Automatic Interpretation of Engineering Schemes for Large Language Model (LLM)-based Risk Assessment<\/a><\/p>\n\n\n\n

Advanced Retrieval Augmented Generation (RAG) for Large Language Model (LLM)-based Risk Assessment<\/a><\/p>\n\n\n\n

Graph Neural Network-Augmented Deep Reinforcement Learning Maintenance Optimization of Energy Infrastructure Networks<\/a><\/p>\n\n\n\n

Multi AI Agent Framework for Learning Causal Bayesian Network Structures<\/a><\/p>\n\n\n\n

Development of AI Agents and Large Language Models for Automated Fault Tree Analysis of Industrial Systems<\/a><\/p>\n\n\n\n

Development of a probabilistic modelling and simulation framework for evaluating the resilience of power grids to floods<\/a><\/p>\n\n\n\n

Integrating community perspectives into flood risk assessment of power grids through dynamic service area modelling<\/a><\/p>\n\n\n\n

Uncertainty analysis within a probabilistic modeling and simulation framework for the risk assessment of power grids exposed to floods<\/a><\/p>\n\n\n\n

Ecology network analysis methods for balancing efficiency and resilience of critical systems and infrastructures<\/a><\/p>\n\n\n\n

Resilience of energy production plants exposed to Natural-Technological (Natech) scenarios of increasing frequency and severity in the climate change context<\/a><\/p>\n\n\n\n

Methods for the evaluation and optimization of the resilience of systems, plants and infrastructures<\/a><\/p>\n\n\n\n

Climate change impact on the risk assessment of energy production plants<\/a><\/p>\n\n\n\n

Advanced Sensitivity Analysis of Groundwater Contaminant Transport Models in Scenarios of Climate Change<\/a><\/p>\n\n\n\n

Enabling the Resilience of Integrated Energy Systems to Tsunami by Early Warning Hazard Nowcasting<\/p>\n\n\n\n

Theses on Prognostics and Health Management for Predictive Maintenance:<\/em><\/strong><\/p>\n\n\n\n

Deep Learning Methods for Extracting Information from Text Documents in Prognostics and Health Management Applications<\/a><\/p>\n\n\n\n

Graph Neural Networks for Anomaly Detection in Controlled Mechanical Systems of Aircrafts<\/a><\/p>\n\n\n\n

Risk-Aware Maintenance Optimization in Energy Components and Systems using Deep Reinforcement Learning and Graph Neural Networks<\/a><\/p>\n\n\n\n

Physics-informed Neural Networks for Fault Prognostics of Equipment<\/p>\n\n\n\n

Transfer Learning Methods for Reliability Predictions in Nuclear Power Systems<\/p>\n\n\n\n

Development of eXplainable Artificial Intelligence (XAI) methods for time series analysis<\/p>\n\n\n\n

Causality-Enhanced Artificial Intelligence for Explainable Predictive Maintenance in the Industry<\/p>\n\n\n\n

Other:<\/em><\/strong><\/p>\n\n\n\n

Well Drilling Location Scheduling Using Deep Reinforcement Learning and Graph Neural Networks for Contaminant Remediation in Nuclear Sites<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Theses with international collaborations: Theses on risk and resilience assessment of complex systems and critical infrastructures: Dynamic RUL Estimates Based on Time Transformation Methods (with SUPSI, Switzerland and Lule\u00e5 University of Technology, Sweden) Development and Uncertainty Quantification of Explainable AI\/ML Models for Critical Heat Flux Prediction in Water\u0002Cooled Nuclear Reactors […]<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"inline_featured_image":false,"footnotes":""},"class_list":["post-5413","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.lasar.polimi.it\/index.php?rest_route=\/wp\/v2\/pages\/5413","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.lasar.polimi.it\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.lasar.polimi.it\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.lasar.polimi.it\/index.php?rest_route=\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/www.lasar.polimi.it\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5413"}],"version-history":[{"count":71,"href":"https:\/\/www.lasar.polimi.it\/index.php?rest_route=\/wp\/v2\/pages\/5413\/revisions"}],"predecessor-version":[{"id":7715,"href":"https:\/\/www.lasar.polimi.it\/index.php?rest_route=\/wp\/v2\/pages\/5413\/revisions\/7715"}],"wp:attachment":[{"href":"https:\/\/www.lasar.polimi.it\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5413"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}