Wednesday, November 20, 8:00am-8:45am
Dr. Enrique López Droguett
University of California, Los Angeles (UCLA)
Presenting in Track 14: Safety Engineering, Risk and Reliability Analysis
Sponsor: SERAD
Presentation Title: Quantum Computing for Risk and Reliability: Opportunities & Outlook
Abstract: Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of reliability and maintainability systems to predict physical asset's performance and aid in integrity management. State of the art monitoring systems now generate large amounts of multidimensional data. Moreover, customers are no longer requiring that their new asset investment be highly reliable; instead, they are requiring that their assets possess the capability to perform fault diagnostics and prognostics and provide alerts when components need to be intervened. With this new Big Data at the engineer’s fingertips, more sophisticated methodologies to handle this data have been developed and expanded within the risk and reliability (R&R) field. Indeed, in the past decade, the availability of powerful computers and special-purpose information processors have led to the development and application of machine and deep learning models for the assessment of R&R of complex engineering systems (CES) that can identify multifaceted and subtle degradation patterns in monitoring data. In recent years, a new computing paradigm has gained momentum: quantum computing, which encompasses the use of quantum mechanical phenomena to perform computations. The power and flexibility of a quantum computer comes from the use of qubits that have the ability to be in a superposition state, or multiple states at once, and share entanglement with each other. By leveraging on these properties, quantum computers can perform operations that are difficult to achieve at scale in classical digital computers. This opens the door to new exciting opportunities for the design and performance assessment of complex engineering systems in general, and for the development of new quantum learning methods for R&R that might be able to recognize intricate interdependent scenarios and components as well as multilayered degradation patterns in CES from multidimensional monitoring data that classical machine learning approaches cannot. In this lecture, we discuss the main concepts underpinning quantum computing and its advantages, disadvantages and potential impact on the risk and reliability assessment of CES. We present state-of-the-art quantum optimization, quantum inference, and machine learning algorithms for developing predictive solutions for risk and reliability assessment of complex engineering systems. We then examine potential opportunities, limitations and challenges for the future development and deployment of quantum computing based R&R solutions for complex engineering systems.
Biography: Dr. Enrique López Droguett is Professor in the Civil and Environmental Engineering Department and Director of the Center for Reliability Science and Engineering in the Garrick Institute for the Risk Sciences at the University of California, Los Angeles (UCLA). He is also Associate Editor for the Journal of Risk and Reliability and serves in the Board of Directors of the International Association for Probabilistic Safety Assessment and Management (IAPSAM). Prof. López Droguett conducts research on Bayesian inference and artificial intelligence supported digital twins and prognostics and health management based on physics informed deep learning for reliability, risk, and safety assessment of structural and mechanical systems. His most recent focus has been on quantum computing for developing predictive solutions for risk and reliability assessment of complex engineering systems. He has led many major studies on these topics for a broad range of industries, including oil and gas, nuclear energy, defense, civil aviation, mining, renewable and hydro energy production and distribution networks. Prof. López Droguett has authored more than 300 papers in archival journals and conference proceedings.