-
An Industrial Perspective
to Machine Learning and Physics for Simulation and Digital Twin
Dr Onur Atak, Siemens STS - Generative AI Supporting Preliminary Engineering Design
Dr Shiva Babu, Rolls-Royce - Physics - informed machine learning : a critique towards robust generalization and interpretability
Dr Zack Xuereb Conti, Alan Turing
Institute
About the speakers:
Keynotes:
Professor Eleni Chatzi, ETH
Eleni Chatzi is
the Chair of Structural Mechanics and Monitoring in the Department of
Civil, Environmental and Geomatic Engineering at ETH Zurich. Her research
interests include the fields of Structural Health Monitoring (SHM), hybrid
modelling and data-driven assessment of engineered systems. Amongst various
positions of trust in this domain, she serves as an editor for major journals
in the field (including Mechanical Systems and Signal Processing, the Journal
of Sound and Vibration, Data Centric Engineering and (nature) Science Reports),
she is the current Vice-President of the European Academy of Wind Energy
(EAWE), and the President of the Swiss Branch of the ECCOMAS association
(SWICCOMAS). She led the recently completed ERC Starting Grant
"WINDMIL" on the topic of Smart Monitoring, Inspection and Life-Cycle
Assessment of Wind Turbines. Her work in the domain of self-aware
infrastructure was recognized with the 2020 Walter L. Huber Research prize,
awarded by the American Society of Civil Engineers (ASCE) and the EASD Junior Research
Prize in the area of Computational Structural Dynamics.
Dr Youngsoo Choi, Lawrence Livermore National Laboratory
Youngsoo is a computational math scientist in Center for Applied Scientific Computing (CASC) under Computing directorate at LLNL. His research focuses on developing efficient reduced order models for various physical simulations for time-sensitive decision-making multi-query problems, such as inverse problems, design optimization, and uncertainty quantification. His expertise includes various scientific computing disciplines. Together with his team and collaborators, he has developed powerful model order reduction techniques, such as machine learning-based nonlinear manifold, space–time reduced order models, and latent space dynamics identification methods for nonlinear dynamical systems. He has also developed the component-wise reduced order model optimization algorithm, which enables fast and accurate computational modeling tools for lattice-structure design. He is currently leading data-driven physical simulation team at LLNL, with whom he developed the open source codes, libROM (i.e., www.librom.net), LaghosROM (i.e., github.com/CEED/Laghos/tree/rom/rom), LaSDI (i.e., github.com/LLNL/LaSDI), gLaSDI (i.e., https://github.com/LLNL/gLaSDI), and GPLaSDI (i.e., github.com/LLNL/GPLaSDI). He earned his undergraduate degree in Civil and Environmental Engineering from Cornell University and his Ph.D. degree in Computational and Mathematical Engineering from Stanford University. He was a postdoctoral scholar at Sandia National Laboratories and Stanford University prior to joining LLNL in 2017.
Industrial/ research centres speakers:
Dr Onur Atak, Siemens
Onur Atak is a
Research Engineering Manager at Siemens Digital Industries Software,
Simulation and Test Solutions (STS) division. He is leading a team of
researchers in the field of Executable Digital Twins (xDT) and 3D Reduced
Order Modelling. In particular, his group is focusing on research topics
such as fast simulation, virtual sensing, model order reduction, machine
learning methods, LLMs and more…
Dr Shiva Babu, Rolls-Royce
Dr Shiva Babu is a Design Automation Engineer at Rolls-Royce
Plc, experienced in development of design systems using AI/ML approaches
supporting whole sub-system and component engineering. He currently leads
technical activities under various work packages in R&T projects, such as
development of AI techniques to improve the effectiveness of design space
exploration, development of Machine Learning Techniques for geometry/feature
(such as damages) recognition to support design systems deployment and
inspection. He is also the Product Owner for several of the key applications in
the team tools portfolio. He received his Ph.D. in knowledge-based engineering
from Birmingham City University in 2016. He was a post-doc at Birmingham city
university in partnership with Brandaeur (Largest contract presswork and
stamping companies in Europe) funded by Innovate UK, investigating tool wear
and its impact on part characteristics. His research interests include
application of AI/Machine learning techniques for design space exploration,
Image processing techniques to support product development lifecycle and
process optimisation.
Dr Zack Xuereb Conti, Alan Turing Institute
Zack is a Turing Research Fellow at The Alan Turing Institute (ATI) and a Visiting Research Fellow within the Department of Civil Engineering at the University of Cambridge. Prior to this, Zack was a Postdoctoral Research Associate within the Data - Centric Engineering programme at the ATI. He completed his PhD within the Architecture and Sustainable Design (ASD) department at the Singapore University of Technology and Design (SUTD), during which period he also conducted research as a visiting scholar at the Harvard Graduate School of Design in Cambridge, USA. Zack's PhD specialised in the application of Bayesian inference at the intersection of architectural design and structural engineering. Zack also holds an MPhil degree in Digital Architectonics from the University of Bath and a Bachelor’s degree in Architecture and Civil Engineering from the University of Malta. He is also a registered architect and civil engineer and has practiced with several architectural design offices.
Zack’s current research interests focus around leveraging Physics in machine - learning for the modeling of dynamical systems across engineering applications. He is interested in developing cutting - edge methodologies to address model generalization and promote physical interpretability.