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Learning from Physics and Data for Design and Condition Monitoring of Engineering Systems

Learning from Physics and Data for Design and Condition Monitoring of Engineering Systems

Optimisation Community Online Event

Monday 8th November 2021

08:00 PST (Los Angeles), 11:00 EST (New York)
16:00 GMT (London), 17:00 CET (Berlin)




Welcome & Overview

Trudy Hoye, NAFEMS Technical Working Groups Manager

Introduction to the Optimisation Working Group

Dr Nadir Ince, Optimisation Working Group Chair


Dr Laura Mainini

Question & Answer Session


Event Description

Data-driven methods and machine learning paradigms introduce great opportunities to learn and tailor models of engineering systems and processes suitable for multi-query, time and resource constrained computational tasks. However, data-driven learning techniques mostly require a huge amount of training data which can be very expensive to obtain, gather and process for most applications in science and engineering. In addition, their predictions are commonly hard to characterize in terms of interpretability, robustness and reliability, which are critical to real-life decision making.

This seminar presentedformulations for physics-based machine learning that allow users to cope with small data and offer avenues to hack interpretability issues. Case studies with applications to design and condition monitoring of engineering systems will be reviewed and discussed.

About this event

This event was hosted by the NAFEMS Optimisation Working Group. The Optimisation Working Group has formed an online Community to help disseminate best practice and encourage the adoption of optimisation methods and technology. For more information and to get involved go to the Optimisation Community webpage.

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This event was available for free to members of the NAFEMS Optimisation Community.


Dr Laura MaininiDr Laura Mainini

Dr Laura Mainini is a leader in multidisciplinary modelling, design and integration of aerospace systems and vehicles. Dr Mainini has professional experience in industrial and academic research as a principal investigator at Collins Aerospace, Raytheon Technologies (former United Technologies Research Center, UTRC) and adjunct professor at Politecnico di Torino where she has been visiting professor and principal investigator/tutor, and continues to supervise PhD theses. Before joining UTRC, Dr Mainini was a postdoctoral associate at the Massachusetts Institute of Technology (MIT) where she developed dynamic data driven methods for self-aware aerospace vehicles. Additionally, she was principal instructor at the Singapore University of Technology and Design (SUTD) for the MIT-SUTD Collaboration Program.

Dr Mainini's interests include digital engineering, data-to-decision, multifidelity and multisource methods, model reduction, and physics-based machine learning for multidisciplinary design and condition monitoring of aerospace systems and vehicles. Dr Mainini is Associate Fellow of the AIAA and a Member of the Royal Aeronautical Society. She serves on the AIAA Multidisciplinary Design Optimization Technical Committee, the AIAA Digital Engineering Integration and Outreach Committee, and several NATO research groups. Laura earned her BSc, MSc. and PhD in Aerospace Engineering from Politecnico di Torino; she received a Fulbright grant to conduct research at MIT during her doctoral studies. In addition, she obtained a MSc. in Aeronautical Engineering from Politecnico di Milano and graduated from the multidisciplinary honour program of the Alta Scuola Politecnica.