Ali Kashefi

Ph.D., Stanford University
U.S. permanent resident
kashefi@stanford.edu
Google Scholar
ORCiD
Twitter
Github

Education

Ph.D., Civil & Environmental Engineering, Stanford University
M.Sc., Civil & Environmental Engineering, Stanford University
M.Sc., Mechanical Engineering, Stanford University
M.Sc., Engineering Mechanics, Virginia Tech
B.Sc., Mechanical Engineering, Sharif University of Technology

Erdős Number

I have Erdős number 3 due to a shared authorship article with Prof. Leonidas Guibas.
Here is the chain:
Paul Erdős → Andrew Odlyzko → Leonidas Guibas → me

Professional Services

  • Reviewer of (1) Journal of Computational Physics, (2) Computer Methods in Applied Mechanics and Engineering, (3) Physics of Fluids, (4) Nature Communications, (5) Archives of Computational Methods in Engineering, (6) Energy, (7) Aerospace Science and Technology, (8) Computers in Biology and Medicine, (9) Engineering with Computers, (10) Applied Ocean Research, (11) Computers & Geosciences, (12) Expert Systems with Applications, (13) Physica A: Statistical Mechanics and its Applications, (14) Earth Science Informatics, (15) Numerical Heat Transfer, Part A: Applications, (16) Numerical Heat Transfer, Part B: Fundamentals, (17) International Journal of Computational Fluid Dynamics, (18) International Journal of Numerical Methods for Heat & Fluid Flow, (19) Applied Mathematics-A Journal of Chinees Universities, (20) AIMS Mathematics, (21) Frontiers in Robotics and AI, (22) Humanities & Social Sciences Communications, (23) Advanced Modeling and Simulation in Engineering Sciences, (24) F1000 Research

  • Member of American Physical Society and American Geophysical Union

    Publications

  • Deep Learning + Computational Physics

    [17] A. Kashefi, "Kolmogorov-Arnold PointNet: Deep learning for prediction of fluid fields on irregular geometries", arXiv, (2024) doi

    [16] A. Kashefi, "A misleading gallery of fluid motion by generative artificial intelligence", Journal of Machine Learning for Modeling and Computing, Begell House, 5 (2024) doi

    [15] Featured A. Kashefi & T. Mukerji, "A novel Fourier neural operator framework for classification of multi-sized images: Application to three dimensional digital porous media", Physics of Fluids, AIP, 36 (2024) doi

    [14] A. Kashefi & T. Mukerji, "ChatGPT for programming numerical methods", Journal of Machine Learning for Modeling and Computing, Begell House, 4 (2023) doi

    [13] A. Kashefi, L. Guibas & T. Mukerji, "Physics-informed PointNet: On how many irregular geometries can it solve an inverse problem simultaneously? Application to linear elasticity", Journal of Machine Learning for Modeling and Computing, Begell House, 4 (2023) doi

    [12] A. Kashefi, "Deep learning algorithms for computational mechanics on irregular geometries", Ph.D. Dissertation, Stanford University, (2023)

    [11] A. Kashefi & T. Mukerji, "Prediction of fluid flow in porous media by sparse observations and physics-informed PointNet", Neural Networks, 167 (2023) doi

    [10] A. Kashefi & T. Mukerji, "Physics-informed PointNet: A deep learning solver for incompressible flows and thermal fields on multiple sets of irregular geometries", Journal of Computational Physics, Elsevier, 468 (2022) doi

    [9] Featured A. Kashefi & T. Mukerji, "Point-cloud deep learning of porous media for permeability prediction", Physics of Fluids, AIP, 33 (2021) doi

    [8] Editor’s Pick A. Kashefi, D. Rempe & L. Guibas "A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries", Physics of Fluids, AIP, 33 (2021) doi

  • Computational Mathematics

    [7] A. Kashefi, "A coarse-grid projection method for accelerating incompressible MHD flow simulations", Engineering with Computers, Springer, 38 (2022) doi

    [6] A. Kashefi, "A coarse grid projection method for accelerating free and forced convection heat transfer computations", Results in Mathematics, Springer, 75 (2020) doi

    [5] A. Kashefi, "Coarse grid projection methodology: A partial mesh refinement tool for incompressible flow simulations", Bulletin of the Iranian Mathematical Society, Springer, 46 (2020) doi

    [4] A. Kashefi, "A coarse-grid incremental pressure projection method for accelerating low Reynolds number incompressible flow simulations", Journal of Computer Science, Springer, 3 (2020) doi

    [3] A. Kashefi & A. Staples, "A finite-element coarse-grid projection method for incompressible flow simulations", Advances in Computational Mathematics, Springer, 44 (2018) doi

  • Computational Mechanics

    [2] A. Kashefi, "Spring-slider and finite element modeling of microseismic events and fault slip during hydraulic fracturing", (2020) doi

    [1] A. Kashefi, M. Mahdinia, B. Firoozabadi, M. Amirkhosravi, G. Ahmadi & M.S. Saidi, "Multidimensional modeling of the stenosed carotid artery: A novel CAD approach accompanied by an extensive lumped model", Acta Mechanica Sincia, Springer, 30 (2014) doi

    Invited Talks

    [6] "ChatGPT for programming numerical methods", School of Engineering and Applied Sciences, Harvard University, Jun 2023
    [5] "ChatGPT for programming numerical methods", Machine Learning Seminar, Department of Applied Mathematics, Brown University, May 2023, slides, YouTube video
    [4] "Physics-informed PointNet", Department of Mathematics, UCLA, Apr 2023
    [3] "Physics-informed PointNet", Machine Learning Seminar, Department of Applied Mathematics, Brown University, Mar 2022, slides, flyer, YouTube video
    [2] "Physics-informed PointNet", Fluid Mechanics Seminar, Sorbonne University (France), Mar 2022, flyer
    [1] "Point-cloud deep learning of computational mechanics", Machine Learning Seminar, Department of Applied Mathematics, Brown University, Nov 2020, slides
    [0] "Convolutional neural networks for fluid flow prediction", Thermal and Fluid Sciences Industrial Affiliates Program, Stanford University, Mar 2018, slides