Imperial College London


Faculty of EngineeringDepartment of Aeronautics

Chair in Aerospace Computational Design



+44 (0)20 7594 8376l.mainini




313bCity and Guilds BuildingSouth Kensington Campus





Research Interests: Multidisciplinary Design Optimization, Complex Multidisciplinary Systems Design and Integration, Scientific Machine Learning, Multifidelity Methods, Model Reduction, Data-to-Decision, Digital Twins, Digital Engineering, Sustainable Aviation

Research Applications: Design, integration, manufacturing and health monitoring of complex multi-physics systems, structures and vehicles; Dynamic reconfigurable mission planning capabilities for intelligent vehicles

Professor Laura Mainini holds the Chair in Aerospace Computational Design at the Department of Aeronautics and is Associate Director of the Brahamal Vasudevan Institute for Sustainable Aviation, where she is responsible for industrial engagement and collaborations.

Before joining Imperial College, Dr. Mainini was Chief Technologist at Collins Aerospace, Applied Research & Technology where she led programs on multidisciplinary and AI-enhanced modelling, design, and integration of aerospace systems. Dr. Mainini has professional experience in industry and academia across Europe, the United States and Singapore. Her academic career comprises appointments at the Massachusetts Institute of Technology, Politecnico di Torino and the Singapore University of Technology and Design.

Prof. Mainini is passionate about developing solutions for the sustainable development of air transportation and space exploration, with focus on advanced computational and mathematical methods that permit to safely unlock novel designs. Her research brings together aerospace engineering and computational science with particular interest and acknowledged contributions in realtime data-to-decision, digital engineering, scientific machine learning and multidisciplinary optimization for design, manufacturing, health monitoring and dynamic reconfigurable mission planning of complex engineering systems, structures and vehicles. Among those, computational frameworks for physics-based machine learning and data assimilation, multifidelity methods for active learning and optimization, and first-in-kind bypass schemes for resource-constrained data analytics and scalable digital twins to assist decisions in safety-critical scenarios have been mostly adopted.

In addition, in pursuit of her dream of becoming an astronaut, Dr. Mainini has been establishing and coordinating international collaborations which led to internationally awarded concepts for human exploration and settlement on Mars; also she was among the select chosen to enter the highly competitive 2021-22 ESA astronaut selection process.

Prof. Mainini is Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA) and Member of the Royal Aeronautical Society. She serves with leadership roles on the AIAA Multidisciplinary Design Optimization Technical Committee, the AIAA Digital Engineering Integration and Outreach Committee, and several international task groups. Dr. Mainini 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 with focus on management of innovation.



Di Fiore F, Mainini L, 2024, Physics-aware multifidelity Bayesian optimization: a generalized formulation, Computers and Structures, Vol:296, ISSN:0045-7949

Di Fiore F, Berri PC, Mainini L, 2024, FREEDOM: Validated Method for Rapid Assessment of Incipient Faults of Aerospace Systems, Aiaa Journal, Vol:62, ISSN:0001-1452, Pages:776-790

Di Fiore F, Mainini L, 2023, Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design, Scientific Reports, Vol:13, ISSN:2045-2322


Mainini L, Diez M, Digital Twins and their Mathematical Souls, AVT-369 Research Symposium on Digital Twin Technology Development and Application for Tri-Service Platforms and Systems, NATO STO

Di Fiore F, Berri PCC, Mainini L, 2023, Multifidelity Framework for the Efficient Identification of Damages in Complex Aerospace Systems, AIAA AVIATION 2023 Forum, American Institute of Aeronautics and Astronautics

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