Hi there 👋, I'm Théo Duez

Passionate by research, learning Mathematics 📈, Physics ⚛️and Computer Science 💻

I'm a Master student 👨‍🎓, currently enrolled both at CentraleSupélec with a conccurent degree at Paris-Saclay Univeristy in the Analysis-Modelisation-Simulation master's program.

Languages and Tools:

Programming Languages:
Scientific Tools
Techs:

Some knowledge or experience in :

Differents projects I have worked on :

From November 2022 to March 2023, I worked on simulation of two phase flows through the Lattice Boltzmann method in the C++ code LBM_saclay during an internship at CEA.
From April 2023 to August 2023, during an internship in the group Geometric Numerical Integration and Reduced Complexity Modelling (in the division Numerical Modeling in Plasma Physics at Max Planck), I worked on Scientifique Machine Learning (PINN's). I focused on different architectures as HNN, LNN, SympNet, RNN and LSTM with contributions in different Julia packages
- GeometricMachineLearning.jl,
- AbstractNeuralNetworks.jl,
- EulerLagrange.jl,
- SymbolicNeuralNetwork.jl.

Théo Duez's Projects

abstractneuralnetworks.jl

Abstract data structures for the construction of neural networks.

diffeqbase.jl

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

eulerlagrange.jl

Code generation for Euler-Lagrange equations.

fermigoldenrule

Short review and simulation in Julia of the Fermi's Golden Rule

geometricmachinelearning.jl

Structure Preserving Machine Learning Models in Julia

master-projects

Somes Projects/Homeworks done during my second year of Master

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)