E-mail: arjitseth AT gmail DOT com

Education

Research Interests and Technical Skills

Applications: Scientific machine learning, physical oceanography, computational aerodynamics, hydrodynamics

Programming Languages: Julia, Python (JAX, PyTorch), MATLAB, C++, Haskell, Fortran, Lua, $\LaTeX$, Git

Software Contributions: Oceananigans.jl, SciMLBenchmarks.jl, AeroFuse.jl

Software: OpenFOAM, ANSYS Fluent and Mechanical, CATIA, SolidWorks, MATLAB, AVL, XFOIL, OpenMDAO, ADflow, DAFoam

Professional Experience

Givens Associate, Mathematics and Computer Science Division (CELS), Argonne National Laboratory, Illinois, USA.

Duration: Jun. 2026 – Aug. 2026

Scientific Computing for Oceanography: Development of UnstructuredOceans.jl, a Julia package for performing oceanographic simulations on unstructured grids with GPU acceleration.

Graduate Research Assistant, The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA.

Advisor: Prof. Patrick Heimbach

Duration: Sep. 2023 – Present

Physical Oceanography: Developing adjoint-based data assimilation methods for stochastic earth-system models.

• Validation and verification of NumericalEarth.jl and Oceananigans.jl, GPU-compatible earth-system computational models in Julia for simulating large-scale coupled ocean-atmosphere-sea-ice general circulation models.
• Executed large-scale ocean circulation simulations on HPC GPU clusters (TACC Lonestar6/Vista) using CUDA.
• Implementing continuous adjoint optimization with finite-element methods to solve inverse problems in ocean models.
• Formulating stochastic approximation models toward large-scale simulations of multi-scale dynamical systems.

Digital Twins: Developing deep Gaussian process regression models for multiphysics simulation and control of vehicles.

• Constructed uncertainty quantification techniques for neural differential equations with Gaussian process regression.
• Performed forecasting of aeroelastic behavior with uncertainty quantification via deep learning, achieving 98% accuracy.
• Co-authored and secured a $100K research grant from RTX Corporation for machine learning of aerostructural systems.

Software Developer, OCTAD Lab, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.

Supervisor: Prof. Rhea Liem

Duration: Sep. 2020 – Jul. 2023

Aircraft Design: Developed AeroFuse.jl, an aircraft design optimization software for undergraduate and graduate curricula.

• Designed interactive educational tools across multiple academic deployments, reaching 200+ students.
• Coupled aerodynamics, structures, propulsion, and flight dynamics PDE solvers for performing aircraft design analyses.
• Implemented adjoint differentiation, improving computational efficiency by 35% vs. automatic differentiation methods.
• Co-authored successful proposal; secured $70K from the HKUST Teaching & Learning Innovation Projects initiative.

Flight Performance: Formulated a data-enhanced method for performing flight simulations with machine learning.

• Developed a Python package to conduct flight performance analyses, validating simulations against real-world data provided by Cathay Pacific Airlines.
• Predicted fuel burn and flight times of regional flights in Hong Kong with 94% accuracy to inform airline operations.

Publications (~120 citations)

Peer-Reviewed Articles

1. Yuan Lyu, Arjit Seth, and Rhea P. Liem. “Fuel consumption and trailing-edge noise tradeoff studies via mission-based airfoil shape optimization”. Aerospace Science and Technology 151 (2024), p. 109331. https://doi.org/10.1016/j.ast.2024.109331
2. Stéphane Redonnet, Turzo Bose, Arjit Seth, and Larry K. B. Li. “Airfoil Self-Noise Prediction Using Deep Neural Networks”. Engineering Analysis with Boundary Elements 159 (Feb. 2024), pp. 180-191. https://doi.org/10.1016/j.enganabound.2023.11.024
3. Arjit Seth, Stephane Redonnet, and Rhea P. Liem. “MADE: A Multidisciplinary Computational Framework for Aerospace Engineering Education”. IEEE Transactions on Education 66.6 (2023), pp. 622-631. https://doi.org/10.1109/TE.2023.3281825
4. Vinay Madhusudanan, Arjit Seth, and G. Sudhakara. “Descending endomorphisms of some families of groups”. In: Applied Linear Algebra, Probability and Statistics: A Volume in Honour of CR Rao and Arbind K. Lal. Springer, 2023, pp. 409-424.
5. Vinay Madhusudanan, G. Sudhakara, and Arjit Seth. “Descending endomorphism graphs of groups”. AKCE International Journal of Graphs and Combinatorics 20.2 (2023), pp. 148-155. https://doi.org/10.1080/09728600.2023.2234956
6. Vinay Madhusudanan, Arjit Seth, and G. Sudhakara. “Descending Endomorphisms of Groups”. Palestine Journal of Mathematics 12.1 (2023), pp. 318-325.
7. Dajung Kim, Arjit Seth, and Rhea P. Liem. “Data-enhanced dynamic flight simulations for flight performance analysis”. Aerospace Science and Technology 121 (2022), p. 107357. https://doi.org/10.1016/j.ast.2022.107357
8. Arjit Seth and Rhea P. Liem. “Amphibious Aircraft Developments: Computational Studies of Hydrofoil Design for Improvements in Water-Takeoffs”. Aerospace 8.1 (2021), p. 10. https://doi.org/10.3390/aerospace8010010

Conference Proceedings

1. Arjit Seth et al. “Portable, High-Performance and Differentiable Ocean Simulations with Unstructured Grids in Julia”. In: The International Conference for High Performance Computing, Networking, Storage, and Analysis. (Peer-reviewed) Accepted. 2026.
2. Dajung Kim, Arjit Seth, and Rhea P. Liem. “Geometric Programming for Airfoil Shape Optimization With Geometric Constraints”. In: AIAA SCITECH 2025 Forum. https://doi.org/10.2514/6.2025-0653
3. Arjit Seth and Tan Bui-Thanh. “An efficient and accurate deep learning approach to weather prediction”. In: EGU General Assembly 2024. 2024. https://doi.org/10.5194/egusphere-egu24-11884
4. Vinay Madhusudanan, Arjit Seth, and G. Sudhakara. “Descending Endomorphisms of Some Families of Groups”. In: Proceedings of Applied Linear Algebra, Probability and Statistics (ALAPS), Springer. In press. 2022.
5. James M. Shihua, Arjit Seth, Ye Li, and Rhea P. Liem. “Experimental and Computational Analyses of Take-off Hydrodynamics of an Amphibian Aircraft Hull”. In: AIAA AVIATION 2020 FORUM. 2020, p. 3174. https://doi.org/10.2514/6.2020-3174
6. Arjit Seth and Rhea Liem. “Hydrofoil Conceptual Design and Optimization Framework for Amphibious Aircraft”. In: AIAA AVIATION 2019 FORUM. Dallas, Texas, USA, 2019, p. 3552. https://doi.org/10.2514/6.2019-3552
7. Arjit Seth and Rhea Liem. “Takeoff analysis of amphibious aircraft with implementation of a hydrofoil”. In: Structures18 - The 2018 Structures Congress. Incheon, South Korea, 2018.

Invited Talks

1. Arjit Seth and Rhea Liem. AeroMDAO – A Multidisciplinary Aircraft Design Platform for Education. Teaching and Learning Symposium, Center for Education Innovation, The Hong Kong University of Science and Technology. Hong Kong, June 2022. https://youtu.be/_H5ig2tr7S4
2. Arjit Seth and Rhea Liem. AeroMDAO – An Educational Aircraft Design Platform. ELITE Community of Practice Symposium of Education Innovation and Technology, Chinese University of Hong Kong. Hong Kong, June 2022.

Master’s Thesis

• Arjit Seth. “Development of a computational design framework for amphibious aircraft”. 2020. https://hdl.handle.net/1783.1/109106

Academic Experience and Awards

MPhil Graduate, OCTAD Lab, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.

Supervisor: Prof. Rhea Liem, Ph.D

Duration: Sep. 2018 – Sep. 2020

Thesis – A Computational Design Framework for Amphibious Aircraft

• Development of a conceptual design and sizing framework for amphibious aircraft with hydrofoils.
• Development of computational models for water-takeoff analyses with hull resistance.
• Multiphase CFD analyses of hydrofoils with cavitation models for generation of surrogates.
• CFD verification of hull performance parameters against experimental water tank tests.
• Investigation and optimisation of aircraft stability and trim in the water-takeoff regime.

Previously an intern at the OCTAD Lab under HKUST’s International Visiting Internship Student Programme.

Head of Aerodynamics, AeroMIT - Aeromodelling Team, Manipal Institute of Technology, Manipal, India.

Duration: Apr. 2016 — Apr. 2017

SAE Aero Design (Micro Class) competition sponsored by Lockheed Martin: Responsible for design and development of a radio-controlled aircraft that fits into a cylinder of 6 inches in diameter. Scoring is based on maximising payload fraction in flight, minimising cylinder length, and a technical report and presentation.

Developments —

• Weight estimation, sizing, payload optimisation, performance, stability and structural analyses of aircraft.
• Computational fluid dynamics analyses of high-lift airfoils and wings.
• Preparation of technical design report and presentation.
• Teaching aerodynamics, flight dynamics, aircraft design and CFD to juniors of the team.

Results —

1. 2018 East: Rank 1 in Design and Rank 3 in Presentation.
2. 2017 West: Rank 1 in Highest Payload Lifted, Rank 2 in Highest Payload Fraction, Rank 4 Overall.
3. 2016 East: Rank 3 in Highest Payload Lifted, Rank 4 in Highest Payload Fraction, Rank 5 Overall.

Protean UAV Challenge sponsored by TATA Sons GTIO and Indian Institute of Technology, Bombay: Responsible for development of a multi-rotor aircraft able to switch between quad, hex and octo configurations mid-air while remaining stable.

• Mathematical model development to ensure maneuvering stability between configurations using MATLAB and Simulink.
• Computational structural analyses to ensure sufficient rigidity.

Results — 1st prize winner.