Resources

Scientific Identity

Trained in theoretical and numerical fluid dynamics, I am currently engineering two-point correlation equation models for turbulence closures (RANS) using channel flow DNS datasets at ONERA. With a strong track record in academic research, international mobility and self-taught machine learning, I aim to solve complex physics problems using rigorous computation and next-generation AI.

Academic CV
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Academic Trajectory

(Prospective) From August 2026
College of Design and Engineering — National University of Singapore (NUS)
Academic Course

M.Eng in Mechanical Engineering by Research. Supervised by Prof. Gianmarco Mengaldo. Physics-driven emulation for extreme climate events.

College of Design and Engineering — National University of Singapore (NUS)
From March 2026
ONERA, The French Aerospace Lab
Research Appointment

Research Intern on two-point turbulence equation closures (RANS) using DNS datasets.

ONERA, The French Aerospace Lab
September 2025 — February 2026
Laboratory of Mechanics Paris-Saclay (LMPS) — University of Paris-Saclay
Research Appointment

ERC DREAM-ON project. Data assimilation via Kalman filtering & physics-augmented neural networks.

Laboratory of Mechanics Paris-Saclay (LMPS) — University of Paris-Saclay
May 2025 — August 2025
Laboratory of Fluid Dynamics — University of Buenos Aires (UBA)
Research Appointment

Experimental microfluidics, Marangoni flows, and thermal capillary networks.

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Laboratory of Fluid Dynamics — University of Buenos Aires (UBA)
From September 2023
M.Sc. in Engineering — ENSTA Paris
Academic Course

Focus on Dynamical Systems, Compressible Fluids, Turbulence, and CFD. Top 5% ranking.

M.Sc. in Engineering — ENSTA Paris

Skills

Computation & Physics

Fluid Mechanics (RANS, DNS, LBM)
Finite Element Method (FEM)
Dynamical Systems
Data Assimilation (Kalman Filters)

AI & Scientific Computing

PyTorch
TensorFlow
Physics-Informed Neural Networks (PINNs)
XGBoost
MATLAB
Optimization

Languages & Core Engineering

Go
C/C++
Python
OCaml
Git
Fusion360

Hobbies

Climbing & bouldering
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Hiking & outdoors exploration
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Travel & international immersion
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Core Research Pillars

Focused research across computational fluid mechanics, AI emulation, and climate scale systems.

AI for Physics Emulation

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AIerolab

Simulating the flow around an airfoil using Physics-Informed Machine Learning (PINNs), and evaluating surrogate models for rapid aerodynamic characterization.

  • March 2025 — June 2025
  • Methodology:  Developing a neural network for physics, rigorously conducting numerical experiments
3D Modeling
Aerodynamics
Physics-Informed Neural Network
Read report
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Real-time data assimilation for health monitoring of mechanical structures

DREAM-ON ERC Project: Real-time structural health monitoring (SHM) with Kalman filtering to build a high-fidelity digital twin ; Machine Learning-based material modeling

Physics-Constrained Neural Network
Constitutive Relation Error
Data Assimilation
Inverse Problem

Climate & Geophysical Systems

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Hackathon: Water Prediction Systems

Machine Learning pipeline to predict regional water shortages combining massive environmental datasets with XGBoost and temporal networks.

XGBoost
Data prep
Geodata

Fluid Mechanics & Simulation

ONERA Turbulence Closures

Engineering two-point correlation equation models for turbulence closures (RANS) directly utilizing top-tier channel flow DNS datasets.

  • From March 2026
  • Methodology:  Managing petabyte-scale simulation databases and deriving statistically robust closure terms.
DNS
RANS
Turbulence Modeling
Fluid Mechanics
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International Physicists' Tournament: Oscillatory Dynamics of Dripping Honey

Physics experiments and theoretical analysis to characterize the coiling and droplet dynamics of honey.

  • September 2024 — February 2025
  • Methodology:  Autonomously conducting experimental methodology and theoretical formulation
Physics
Experiments
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LBM Simulations for RANS Verification

Lattice-Boltzmann Method (LBM) solver developed to explore influence parameters and validate standard experimental benchmarks.

  • May 2025 — May 2025
  • Methodology:  Constructing computationally stable LBM solvers for complex boundary conditions
Python

Contact me

Always open to discussing my projects, exchanging ideas on fluid mechanics and AI, or exploring research collaborations. If you're a fellow researcher, recruiter, or just curious about the work, drop me a message below!

2026 — Théo Vidal