About

Charbel Farhat is the Vivian Church Hoff Professor of Aircraft Structures and a Professor of Aeronautics and Astronautics at Stanford University. His research focuses on the fields of aeronautics and astronautics, with particular emphasis on aircraft structures. As a faculty member, he contributes to advancing knowledge in these areas through his academic and research activities.

Research topics

• Artificial Intelligence
• Computer Science
• Mathematics
• Applied mathematics
• Algorithm
• Mathematical analysis
• Physics

Selected publications

• A new surrogate microstructure generator for porous materials with applications to the buffer layer of TRISO nuclear fuel particles

  Journal of Nuclear Materials · 2026-02-03

  articleOpen access
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• Probabilistic learning from real-world observations of systems with unknown inputs for model-form UQ and digital twinning

  Computer Methods in Applied Mechanics and Engineering · 2025-03-11 · 3 citations

  articleSenior author
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• Permeability Modeling of the Mars 2020 Parachute Broadcloth Material

  AIAA Journal · 2025-08-06

  article

  The broadcloth material used in parachute manufacturing is generally a thin, woven, permeable textile. The small length scales of fibers, pores, and gaps in fabric are challenging to spatially resolve in a full-scale parachute simulation. In this work, simulations are performed using a 3D reconstruction of the broadcloth material used in the Mars 2020 mission, and simulation results using the detailed reconstructed geometry are compared to a simplified model proposed in a previous work. Furthermore, results from simulations under Earth ambient lab conditions are compared to experimental permeability test data to validate the choice of parameters for this reduced-order model. Simulations under ASPIRE SR03 flight-relevant conditions are also performed to study permeability in a rarefied flow regime. It is observed that flow through the material is similar to a developing pipe flow, and under low-density conditions, significant slip velocity is present inside pores. For all conditions investigated, the pressure drag is the primary contributor to the total drag force. Drag and mass flow rate discrepancies are observed between models, motivating future work to investigate the sensitivity of system-level parachute FSI simulations to the assumed permeability model and associated parameters.

  PublisherDOI

• Projection-based model order reduction of embedded boundary models for CFD and nonlinear FSI

  Computer Methods in Applied Mechanics and Engineering · 2025-03-16 · 2 citations

  articleSenior author
  PublisherDOI

• Reduced Order Modeling conditioned on monitored features for response and error bounds estimation in engineered systems

  Mechanical Systems and Signal Processing · 2025-01-13 · 7 citations

  articleOpen access

  Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Although purely data-driven methods are available for ROM construction, schemes that allow to retain a portion of the physics tend to enhance the interpretability and generalization of ROMs. However, physics-based techniques can adversely scale when dealing with nonlinear systems that feature parametric dependencies. This study introduces a generative physics-based ROM that is suited for nonlinear systems with parametric dependencies and is additionally able to provide numerical error bounds associated with the respective estimates. A main contribution of this work is the conditioning of these parametric ROMs to features that can be derived from monitoring measurements, feasibly in an online fashion. This is contrary to most existing ROM schemes, which remain restricted to the prescription of the physics-based, and usually a priori unknown, system parameters. Our work utilizes conditional Variational Autoencoders to continuously map the required reduction bases to a feature vector extracted from limited output measurements, while additionally allowing for a probabilistic assessment of the ROM-estimated Quantities of Interest. An auxiliary task using a neural network-based parametrization of suitable probability distributions is introduced to re-establish the link with physical model parameters. We verify the proposed scheme on a series of simulated case studies incorporating effects of geometric and material nonlinearity under parametric dependencies related to system properties and input load characteristics.

  PublisherDOI

• A Tractable Nonparametric Probabilistic Approach for Modeling and Quantifying Model-Form Uncertainty in Turbulent Cfd

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Projection-Based Model Order Reduction of Adaptively Refined Large-Scale Turbulent Flows

  2025-01-03

  articleSenior author

  A fundamental limitation of current snapshot-based model order reduction approaches such as projection-based model order reduction (PMOR) is the widespread acceptance that the snapshot data generated by the parametric high-dimensional model (HDM) and used to build the reduced-order model are represented in equidimensional spaces. For many applications featuring regions of disparate solution gradients however, state-of-the-art HDMs compute the sought-after numerical solutions on adapted rather than fixed-size meshes. This is particularly true in the computational fluid dynamics (CFD) simulation of flow problems whose solutions exhibit shocks, boundary layers, vortex shedding, dynamic material interfaces, or other flow features. For such applications, adaptive mesh refinement and coarsening is not only desired for accuracy and/or computational speed, but may also be necessary for computational tractability. Therefore, there is an acute need to enable the PMOR of HDMs constructed on adapted meshes. Filling this gap will greatly enhance the state of the art of PMOR in general and for CFD applications in particular. To this end, this paper builds on the authors' previous works in this area with new and critical contributions that enable the effective PMOR of large-scale CFD models on adapted meshes. Specifically, it presents an efficient approach for computing inner products between solution snapshots pre-computed on adapted meshes; and for constructing reduced-order bases. Furthermore, the paper illustrates these contributions and demonstrates their significance for the PMOR of industrial-grade adaptive CFD analysis with the detached eddy simulation of a dynamic wake of the Ahmed body, for which it reports impressive speedups.

  PublisherDOI

• A tractable nonparametric probabilistic approach for modeling and quantifying model-form uncertainty in turbulent CFD

  Journal of Computational Physics · 2025-05-12 · 4 citations

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• Mesh Adaptation Grounded in a Local Length Scale for Compressible Multi-Material Flows

  2025-01-03

  articleSenior author

  A mesh adaptation strategy with a local (in space and time) length scale is proposed for the accurate numerical simulation of compressible multi-material flows with immiscible fluids. The level set method is used to track the material interfaces and the reinitialization of the level set is performed using a direct approach based on the geometric distance to the nearest material interface. The mesh adaptation criterion is based as usual on the distance to the material interface, but the adaptation threshold and mesh size are scaled using a novel local length scale based on the radius of curvature of the material interface. The efficiency of the proposed mesh adaptation approach is demonstrated for various multi-material flow problems relevant to the numerical simulation of bubble dynamics. The obtained results show that the direct reinitialization of the level set function leads to improved accuracy in mass conservation during the numerical simulation, compared to traditional reinitialization schemes; and the local length scale mesh adaptation criterion leads to significantly lower mass conservation errors during the numerical simulation, compared to the standard global length scale criterion.

  PublisherDOI

• A Tractable Nonparametric Probabilistic Approach for Modeling and Quantifying Model-Form Uncertainty in CFD Computations With Turbulence Modeling

  2025-01-03

  article

  This paper presents an innovative, computationally tractable approach for modeling and quantifying model-form uncertainty (MFU) in viscous computational fluid dynamics (CFD) models. It distinguishes between two sources of uncertainty: those related to turbulence modeling and other sources such as wall and far-field boundary conditions. The proposed approach comprises two complementary and coupled methods for uncertainty quantification (UQ): one targeting uncertainties in Reynolds stress modeling; and the other addressing all remaining model-form and parametric uncertainties. The first method decomposes the Reynolds stress tensor into a trace-vanishing deviatoric component and a spherical part. It then constructs a hyperparameterized probability model for the eigenvalues of the deviatoric component, based on its spectral algebraic properties. Further probabilistic modeling yields a complete hyperparameterized model for the Reynolds stress tensor, with each realization corresponding to an admissible turbulence model within a specific family. The second method adapts a recently developed nonparametric probabilistic approach for modeling and quantifying MFU to the context of this study. It relies on a probabilistic, projection-based model order reduction (PMOR) technique that is also hyperparameterized, ensuring computational tractability for UQ. The hyperparameters for both methods are simultaneously determined by formulating and minimizing an appropriate data-driven probabilistic loss function. Additionally, the methodology accounts for the uncertainties associated with PMOR, which is introduced to achieve efficient Monte Carlo simulations. The efficacy of the overall approach proposed for UQ in large-scale CFD computations is demonstrated through the Reynolds-averaged Navier-Stokes-based aerodynamic analysis of a rigid NASA Common Research Model configuration in the transonic flow regime, for which wind tunnel data is available.

  PublisherDOI

Recent grants

• Planning Grant: Engineering Research Center for Digital Twins in Engineering and Medicine

  NSF · $100k · 2019–2021

• ITR: A Data Driven Environment for Multiphysics Applications

  NSF · $852k · 2004–2007

• DDDAS-TMRP: A Dynamic Data Driven System for Structural Health Monitoring and Critical Event Prediction

  NSF · $825k · 2005–2009

Frequent coauthors

• Radek Tezaur

  Vaughn College of Aeronautics and Technology

  97 shared

• Philip Avery

  CMSoft (United States)

  83 shared

• David Amsallem

  Meta (United States)

  57 shared

• Michel Lesoinne

  University of Colorado Boulder

  48 shared

• Sebastian Grimberg

  27 shared

• Ulrich Hetmaniuk

  Shift (United Kingdom)

  22 shared

• Christian Soize

  Université Paris-Est Créteil

  21 shared

• Bruno Koobus

  21 shared

Education

• Ph.D., Aeronautics and Astronautics

  Stanford University

  1984

• M.S., Aeronautics and Astronautics

  Stanford University

  1980

• B.S., Aeronautical Engineering

  American University of Beirut

  1977

Awards & honors

• Vannevar Bush Faculty Fellowship from the Department of Defe…
• Docteur Honoris Causa degrees from Ecole Normale Superieure…
• 2024 Kuwait Prize laureate
• TAKREEM AMERICA Award recipient
• ISI Highly Cited Researcher in Engineering