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The Journey

Professional Summary

Prof. Dr. Sashikumaar Ganesan

Professor, Indian Institute of Science (IISc), Bangalore
Leading the AI for Research and Engineering eXcellence (AiREX), lab at IISc.
Founder & Chief AI Officer, Zenteiq AiTech Innovations

Prof. Sashikumaar Ganesan focuses on Scientific Machine Learning (SciML), Physics-Informed Neural Networks (PINNs), high-performance computing, and hardware-aware numerical methods. He leads the AI for Research and Engineering eXcellence (AiREX) Lab at IISc, advancing AI applications in scientific and engineering domains.

As Founder and CAiO of Zenteiq.ai, he is developing science-native Foundation Models to enhance AI-driven R&D, aerospace, and defence innovation.

Leadership Roles

Programme Director, Advanced Programs: Gen AI, Computational Data Science and AI, and AI & MLOps (since 2020).
Chairman, CDS Department, IISc (2018–2024).
Co-Convenor, Kotak-IISc AI-ML Centre.
Founding Chair, M.Tech (online). Data Science & Business Analytics.

Academic Background

Ph.D. (Dr. rer. nat.), Computational Mathematics, Otto-von-Guericke University, Germany.
Alexander von Humboldt Fellow, Weierstrass Institute, Berlin.
Research Associate, Dept. of Aeronautics, Imperial College London.

Name: Prof. Sashikumaar Ganesan
Email: sashi@iisc.ac.in
Phone: (+91) 802 293 2902
Address:

Department of Comptational & Data Sciences,
Indian Institute of Science, Bangalore.
Karnataka, India - 560012

Our recent development:
Try pip install fastvpinns

Prof. Ganesan advises on AI integration across aerospace, defence, and manufacturing sectors. His work supports the deployment of AI platforms integrating DataOps, MLOps, and LLMOps workflows to strengthen India’s sovereign AI initiatives.

We aim to build AI systems that not only understand science and engineering but actively contribute to solving scientific challenges.

15+

Years of Experiance

25+

Students Mentored

60++

Published Works

1000+

Professionals Upskilled in AI/ML

Research

Interests

AI for Research and Engineering eXcellence (AiREX) Labis at the forefront of integrating scientific computing with machine learning. Our group's mission is to harmonize the precision of scientific computing with the agility and adaptability of machine learning, paving the way for breakthroughs in both theoretical and applied domains. We delve into:

Artificial Intelligence (AI)

Focused on developing AI-based recommender systems for combat aircraft, with a special emphasis on emitter classification and multi-sensor data fusion. Additionally, there's a key focus on constructing platform engineering frameworks for the integration of diverse ML models. In the educational sector, the approach includes creating AI-driven predictive models based on learners' activities and developing tools for skill and performance assessments.

Scientific Machine Learning (SciML)

Our research is centered on integrating advanced parallel computing techniques, especially high-performance (hp) Physics-Informed Neural Networks (PINNs) and GPU acceleration, to enhance computational efficiency and accuracy. By infusing traditional numerical schemes with data-driven approaches, we align our work with the forefront of SciML trends, ensuring a harmonious blend of scientific precision and technological innovation.

Machine Learning Operations (MLOps)

Crafting scalable ML algorithms suited for diverse computational environments. Innovating in distributed training techniques and cloud computing applications. Implementing rigorous ML model and data version control systems. Establishing efficient CI/CD pipelines for ML deployment and operations.

Computational Science

Specializing in finite element analysis. Advanced modeling of multiphase flows, fluid-structure interactions, turbulent flows, and epidemiological modeling. Developing hybrid CPU-GPU parallel algorithms and hardware-aware scalable parallel implementations.

Research

Publications

Book

S. Ganesan & L. Tobiska : Finite Elements: Theory and Algorithms. Cambridge University Press, 2017, ISBN: 9781108415705.

Journal Articles

T. Anandh, D. Ghose, H. Jain, S. Ganesan: FastVPINNs: Tensor-Driven Acceleration of VPINNs for Complex Geometries. SIAM Journal on Scientific Computing (2024) Acccepted.

T. Anandh, D. Ghose, J. Himanshu, S. Pratham, S. Ganesan, V. John: Improving hp-Variational Physics-Informed Neural Networks for Steady-State Convection-Dominated Problems. Computer Methods in Applied Mechanics and Engineering (CMAME) (2024, revision submitted) arXiv:2411.09329.

T. Anandh, D. Ghose, T. Ankit, G. Abhineet, S. Suranjan, S. Ganesan: An efficient hp-Variational PINNs framework for incompressible Navier-Stokes equations. (2024) arXiv:2409.04143.

T. Anandh, D. Ghose, S. Ganesan: FastVPINNs: An efficient tensor-based Python library for solving partial differential equations using hp-Variational Physics Informed Neural Networks. Journal of Open Source Software (JOSS) (2024) 9(99), 6764.

S. Yadav, S. Ganesan: Artificial Neural Network-augmented stabilized finite element method. Journal of Computational Physics (2024), 499, 112702.

S. Yadav, S. Ganesan: ConvStabNet: A CNN-based approach for the prediction of local stabilization parameter for SUPG scheme. Calcolo (2024), 61, 52.

S. Ganesan, M. K. Singh: Operator-splitting finite element method for solving the radiative transfer equation. Numerical Algorithms (2024).

D. Garg, S. Ganesan: Local projection stabilized finite element methods for advection–reaction problems. Calcolo (2023) 60, Article number: 45.

S. Srivastava, S. Ganesan: A stabilized local projection finite element approximation for computations of Oldroyd-B viscoelastic fluid flows. Int J Adv Eng Sci Appl Math, (2022), 13, 383–393.

D. Garg, S. Ganesan: Generalized local projection stabilized nonconforming finite element methods for Darcy equations. Numerical Algorithms, (2022) 89, 341-369.

D. Garg, S. Ganesan: An overlapping local projection stabilization for Galerkin approximations of Stokes and Darcy flow problems. Applied Numerical Mathematics, (2022), 171, 106-127.

S. Ganesan, D. Subramani, T. Anandh, D. Ghose, GR. Babu: Ensemble forecast of COVID-19 in Karnataka for vulnerability assessment and policy interventions. (2021) medRxiv

S.M. Joshi, T. Anandh, B. Teja, S. Ganesan: On the choice of hyper-parameters of artificial neural networks for stabilized finite element schemes. Int J Adv Eng Sci Appl Math (2021), 13, 278–297.

S. Ganesan, D. Subramani: Spatio-temporal predictive modeling framework for infectious disease spread. (2021), Nature Scientific Reports 11, 6741.

B. Pal, S. Ganesan: An a priori error analysis for a projection based variational multiscale finite element method for Oseen problems in a time-dependent domain. Comput. Math. with Appl., (2021), 82, 130-147.

S. Ganesan, M. Shah: SParSH-AMG: A library for hybrid CPU-GPU algebraic multigrid and preconditioned iterative methods. arXiv, (2020) arXiv:2007.00056

J. Venkatesan, S. Ganesan: Finite element computations of viscoelastic two-phase flows using Local Projection Stabilization. Int. J. Numer. Meth. Fluids, (2020), 92(8), 825-854.

S. Srivastava, S. Ganesan: Local projection stabilization with discontinuous Galerkin method in time applied to convection dominated problems in time-dependent domains. BIT Numerical Mathematics,(2019) 60, 481–507

J. Venkatesan, A. Padmanabhan, S. Ganesan: Simulation of viscoelastic two-phase flows with insoluble surfactants. Journal of Non-Newtonian Fluid Mechanics, (2019) 267, 61-77

J. Venkatesan, S. Ganesan: Computational modeling of impinging viscoelastic droplets. Journal of Non-Newtonian Fluid Mechanics, (2019) 263, 42-60

S. Srivastava, S. Ganesan: On the temporal discretizations of convection dominated convection-diffusion equations in time-dependent domain. Pro Mathematica, (2018) 30, 99-137

J. Venkatesan, S. Ganesan: A three-field local projection stabilized formulation for computations of Oldroyd-B viscoelastic fluid flows. Journal of Non-Newtonian Fluid Mechanics, (2017) 247, 90-106

U. Wilbrandt, C. Bartsch, N. Ahmed, N. Alia, F. Anker, L. Blank, A. Caiazzo, S. Ganesan, S. Giere, G. Matthies, R. Meesala, A. Shamim, J. Venkatesan, V. John: ParMoon - a modernized program package based on mapped finite elements. Computers and Mathematics with Applications, (2017) 74, 74-88

S. Ganesan, S.Srivastava: ALE-SUPG finite element method for convection-diffusion problems in time-dependent domains: Conservative form. Appl. Math. Comp., (2017) 303, 128-145

S. Ganesan, S. Lingeshwaran: Galerkin finite element method for cancer invasion mathematical model. Computers and Mathematics with Applications (2017) 73, 2603-2617

S. Ganesan, S. Lingeshwaran: A biophysical model of tumor invasion. Communications in Nonlinear Science and Numerical Simulation, (2017) 46, 135-152

P. Satish kumar, S. Ganesan: Numerical simulation of nanocrystal synthesis in a microfluidic reactor. Computers and Chemical Engineering, (2017) 96, 128 - 138

B. Pal, S. Ganesan: Projection based variational multiscale method for incompressible Navier--Stokes equations in time-dependent domains. Int. J. Numer. Meth. Fluids, (2017) 84, 19-40

S. Ganesan, V. John, G. Matthies, R. Meesala, S. Abdus, U. Wilbrandt: An object oriented parallel finite element scheme for computations of PDEs: Design and implementation. IEEE 23rd International Conference on High Performance Computing Workshops (HiPCW) (2016), DOI 10.1109/HiPCW.2016.023

J. Venkatesan, S. Rajasekaran, A. Das, S. Ganesan: Effects of temperature-dependent contact angle on the flow dynamics of an impinging droplet on a hot solid substrate. Int. J. Heat and Fluid Flow, (2016) 62, 282-298

S.Mohapatra, S. Ganesan: A Non-Conforming Least Squares Spectral Element Formulation for Oseen Equations with Applications to Navier-Stokes Equations. Numerical Functional Analysis and Optimization, (2016), 37, 1295 - 1311

J. Venkatesan, S. Ganesan: On the Navier-slip boundary condition for computations of impinging droplets. IEEE 22nd International Conference on High Performance Computing Workshops (HiPCW) (2015), 2-11, DOI 10.1109/HiPCW.2015.10

S. Ganesan: Simulations of impinging droplets with surfactant-dependent dynamic contact angle. Journal of Computational Physics, (2015), 301, 178 - 200

B. Pal, S. Ganesan: A finite element variational multiscale method for computations of turbulent flow over an aerofoil. Int. J. Adv. Engg. Sci. Appl. Math., (2015), 7, 14 - 24

S. Ganesan, J. Venkatesan, S. Rajasekaran: Modeling of the non-isothermal liquid droplet impact on a heated solid substrate with heterogeneous wettability. Int. J. Heat Mass Transfer, (2015), 88, 55 - 72

F. Ankera, S. Ganesan, V. John, E. Schmeyer: A comparative study of a direct discretization and an operator-splitting solver for population balance systems. Computers and Chemical Engineering, (2015), 75, 95 - 104

S. Ganesan, S. Rajasekaran, L. Tobiska: Numerical modeling of the non-isothermal liquid droplet impact on a hot solid substrate. Int. J. Heat Mass Transfer, (2014), 78, 670 - 687

S. Ganesan: On the dynamic contact angle in simulation of impinging droplets with sharp interface methods. Microfluidics and Nanofluidics, Springer, (2013), 14, 615-625

S. Ganesan, P. K. Yalavarthy: Modeling of Terahertz Heating Effects in Realistic Tissues. IEEE Journal of Selected Topics in Quantum Electronics (Issue on current trends in terahertz photonics and applications), 2013 19(1), pp.8400908

S. Ganesan, L. Tobiska: Operator-splitting finite element algorithms for computations of high-dimensional parabolic problems. Appl. Math. Comp., (2013) 219, 6182 - 6196

S. Ganesan: An operator-splitting Galerkin/SUPG finite element method for population balance equations: Stability and convergence. ESAIM: Mathematical Modelling and Numerical Analysis (M2AN), (2012) 46,46, 1447-1465

S. Ganesan, L. Tobiska: Arbitrary Lagrangian-Eulerian finite-element method for computation of two-phase flows with soluble surfactants. Journal of Computational Physics, (2012) 231 (9), 3685-3702

M. Krasnyk, M. Mangold, S. Ganesan, L. Tobiska: Numerical reduction of a crystallizer model with internal and external coordinates by proper orthogonal decomposition. Chem. Eng. Sci., (2012) 70, 77-86

S. Ganesan, L. Tobiska: An operator-splitting finite element method for the efficient parallel solution of multidimensional population balance systems. Chem. Eng. Sci., (2012) 69 (1), 59-68

U. Kornek, F. Müller, K. Harth, A. Hahn, S. Ganesan, L. Tobiska, R. Stannarius: Oscillations of soap bubbles. New J. Phys., (2010) 12, 073031

S. Ganesan, L. Tobiska: Stabilization by local projection for convection-diffusion and incompressible flow problems. J. Sci. Comput., (2010) 43 (3), 326-342

S. Ganesan, L. Tobiska: A coupled arbitrary Lagrangian-Eulerian and Lagrangian method for computation of free surface flows with insoluble surfactants. Journal of Computational Physics, (2009) 228 (8), 2859-2873

S. Hysing, S. Turek, D. Kuzmin, N. Parolini, E. Burman, S. Ganesan, L. Tobiska: Quantitative benchmark computations of two-dimensional bubble dynamics. Int. J. Numer. Meth. Fluids, (2009) 60, 1259-1288

S. Ganesan, L. Tobiska: Modelling and simulation of moving contact line problems with wetting effects. Comput. Visual. Sci., (2009) 12, 329-336

S. Ganesan, G. Matthies, L. Tobiska: Local projection stabilization of equal order interpolation applied to the Stokes problem. Math. of Comput., (2008) 77, 2039-2060

S. Ganesan, L. Tobiska: An accurate finite element scheme with moving meshes for computing 3D-axisymmetric interface flows. Int. J. Numer. Meth. Fluids, (2008) 57(2), 119 - 138

S. Ganesan, G. Matthies, L. Tobiska: On spurious velocities in flow problems with interfaces. Comput. Methods Appl. Mech. Engrg., (2007) 196, 1193 - 1202

S. Ganesan, V. John: Pressure separation - a technique for improving the velocity error in finite element discretisations of the Navier-Stokes equations. Appl. Math. Comp., (2005) 165, 275 - 290

Book Chapters

S. Ganesan, A. Hahn, K. Simon, L. Tobiska: Finite Element Computations for Dynamic Liquid-Fluid Interfaces. In: M. T. Rahni, M. Karbaschi and R. Miller, editors, Computational Methods for Complex Liquid-Fluid Interfaces, CRC Press, Taylor and Francis Group, (2015) Ch.16, Pages: 331--351, ISBN: 9781498722087 (online)

S. Ganesan, B. Pal, S.Srivastava: Simulation of two-phase flows with surfactants. In: N. K. Gupta, A. V. Manzhirov, and R. Velmurugan, editor, Topical problems in theoretical and applied mechanics, Elite Publishing House Pvt. Ltd., (2013) Pages: 418 - 425, ISBN: 978-81-88901-55-5

S. Ganesan, Bhanu Teja: A multi-level finite element discretization for efficient solution of multidimensional population balance system. In: S. Sundar, editor, Advances in PDE Modeling and Computation, Ane Books Pvt. Ltd., (2013), Pages: 105 - 118 (online)

S. Ganesan, S. Rajasekaran, L. Tobiska: An ALE-based finite element method for the simulation of an impinging droplet on a hot surface. In: S. Sundar, editor, Advances in PDE Modeling and Computation, Ane Books Pvt. Ltd., (2013), Pages: 35 - 53 (online)

S. Ganesan, L. Tobiska: Finite Element Simulation of an Impinging Liquid Droplet. In: Albrecht Bertram and Jürgen Tomas, editor, Micro-Macro- Interactions In Structured Media and Particle Systems, Springer, (2008) (online)

S. Ganesan, L. Tobiska: A Finite Element Method for the Simulation of a Liquid Droplet Impinging on a Solid Surface. In: Palle Jorgensen, Xiaoping Shen, Chi-Wang Shu and Ningning Yan, editor, RECENT ADVANCES IN COMPUTATIONAL SCIENCES, World Scientific, (2008) (online)