Roberto Rocchetta

Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry

Advancing digital twin implementation: a toolbox for modelling and simulation

Risk-based methods for sustainable energy system planning: A review

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Digital Twin: Benefits, use cases, challenges, and opportunities

Digital twin modeling

Artificial intelligence, machine learning and deep learning in advanced robotics, a review

Energy system resilience – A review

Review on deep learning applications in frequency analysis and control of modern power system

Combined heat and power system intelligent economic dispatch: A deep reinforcement learning approach

Deep reinforcement learning framework for resilience enhancement of distribution systems under extreme weather events

Reinforcement learning in sustainable energy and electric systems: a survey

Digital Twins: State-of-the-Art and Future Directions for Modeling and Simulation in Engineering Dynamics Applications

Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects

Federated learning enabled digital twins for smart cities: Concepts, recent advances, and future directions

A modeling and simulation framework for the reliability/availability assessment of a power transmission grid subject to cascading failures under extreme weather conditions

Evaluating the strategies for sustainable energy planning in Pakistan: An integrated SWOT-AHP and Fuzzy-TOPSIS approach

The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities

On-line Bayesian model updating for structural health monitoring

Risk assessment and risk-cost optimization of distributed power generation systems considering extreme weather conditions

A reinforcement learning framework for optimal operation and maintenance of power grids