Prof. Charbel Farhat

 

Biography
Prof. Charbel Farhat is the Vivian Church Hoff Professor of Aircraft Structures in the School of Engineering at Stanford University, where he also serves in the Institute for Computational and Mathematical Engineering. From 2008 to 2023, he chaired the Department of Aeronautics and Astronautics and directed several major centers, including the Army High Performance Computing Research Center and the Stanford–KACST Center of Excellence. He has also advised the U.S. Air Force Scientific Advisory Board, the Space Technology Industry–Government–University Roundtable, and the U.S. Department of Commerce.

Prof. Farhat is an elected member of the U.S. National Academy of Engineering, the UK Royal Academy of Engineering, and the Lebanese Academy of Sciences. He is a Fellow of leading professional societies, including AIAA, ASME, IACM, SES, SIAM, USACM, and WIF. His distinguished contributions have been recognized with numerous international awards, such as:

- Vannevar Bush Faculty Fellowship
- IEEE Gordon Bell Prize
- IEEE Sidney Fernbach Award
- ASME Lifetime Achievement Award
- IACM Gauss–Newton Medal
- Kuwait Prize in Applied Sciences (2024)

Author of more than 650 refereed publications and a frequent plenary lecturer worldwide, Prof. Farhat is internationally acclaimed for advancing the frontiers of computational mechanics, digital twins, and high-performance computing for aerospace and defense applications.

As a keynote speaker, he brings unmatched expertise in bridging rigorous physics-based modeling with probabilistic frameworks and real-time adaptation—pioneering the next generation of robust digital twins for critical engineering systems.

 

 

Keynote Talk
A Probabilistic, Physics-Based Framework for Robust Digital Twin Development

 

Abstract
A digital twin is commonly defined as a dynamic, virtual replica of a physical asset, process, or system. Unlike static models or disconnected simulations, it is continuously updated through real-time data streams—often originating from sensors and other monitoring sources—enabling the twin to evolve in tandem with its physical counterpart. This live integration supports advanced monitoring, analysis, prediction, and, most importantly, decision-making and optimization throughout the lifecycle of the physical system.

Constructing such technology typically requires combining artificial intelligence, machine learning, and software analytics with physics-based modeling, thereby creating adaptive simulation models that remain synchronized with reality. Early digital twins often focused on combining data analytics with model-based prediction of selected quantities of interest (QoIs). This lecture will critically examine whether a limited set of QoIs can reliably capture the true state of a newly designed and deployed platform.

While the “Digital” side of digital twins is widely understood, the “Twin” presents greater challenges—chief among them the risk of misrepresenting the physical system. To mitigate this, Prof. Farhat introduces a methodology based on adaptable, stochastic, computationally tractable, low-dimensional yet high-fidelity physics-based models rooted in partial differential equations. This framework incorporates strategies for quantifying model-form uncertainty through a multi-component probabilistic approach, alongside projection-based model reduction and machine learning, yielding stochastic physics-based models capable of self-adaptation through sensor data assimilation and real-time operation.

Applications are highlighted through case studies in aerospace, including:

1. Aircraft vibration assessment
2. UAV autonomous landing
3. Condition-based aircraft maintenance
4. In-cockpit prediction of aerodynamic loads

Together, these examples illustrate how probabilistic, physics-driven digital twins can provide robust, real-time insights—transforming the way complex aerospace systems are designed, monitored, and operated.