I am a PhD-level consultant designing, implementing and optimizing bespoke mathe­matical models, algorithms and numerical simulations. My ten years of inter­national experience at the cross­roads of applied mathe­matics and pro­gramming allow me to find the algorithm best suited to your situation, or to optimize codes that crunch a lot of numbers to improve speed, reliability and accuracy (cf. http://mathieu.bouville.name/en/science/numerical-programming.html).

I have worked in fields as varied as materials science and applied physics; investing, personal finance and financial planning; renewable energy and energy savings; sport statistics (and I am always interested in working in new domains).

I have a doctorate degree from the University of Michigan. I co-authored 15 scientific articles on mathematical modeling and numerical simulations, including two as first author in Physical Review Letters.

What are numerical modeling, mathematical algorithms
and computer simulations?

Mathematical modeling: An introduction — Mathematical modeling means taking a concrete problem, and solving it through math, be it by solving equations or developing custom software doing calculations. Modeling may replace experiments or complement them.

A player, a computer and a mathematician try to solve a sudoku puzzle — A sudoku puzzle can be solved by deduction, by brute force or by turning it into a different problem, depending on whether you are a player, a computer or a mathematician.

Monte Carlo simulations: from physics to financial planning — Monte Carlo simulations have diverse applications in physics, materials science, investing, etc. They are used in situations where randomness plays a part (or which can be treated as such).

How to implement calculations efficiently and accurately — Programmers know how to improve the speed and robustness of their codes. However, when a program does a lot of calculations, new problems arise and new solutions are needed.

Modeling and simulations in materials science and applied physics

Monte Carlo simulations

At the University of Cambridge, I developed a new, faster (factor 100) computer simulation method within the framework of Monte Carlo lattice model to predict polymer glass-transition temperatures.

Molecular dynamics simulations

In order to better control dopant diffusion during device processing, I studied the effect of stress on point defects in semi­conductors in collaboration with mechanical engineers.

Working closely with experimentalists, I designed a mathematical model to determine under what conditions pits would form in hetero­epitaxial semi­conductor thin films. [Phys. Rev. B 70, 235312 (2004)]

Phase-field simulations

On a project I initiated, I showed how various parameters affect the interplay between martensite and pearlite formation in steel (which cannot be done experimentally). [Phys. Rev. Lett. 97, 055701 (2006)]

Collaborating with experimentalists, I used phase-field simulations to explain how alloying may stabilize poly­crystalline thin films and dramatically enhance their performance. [Phys. Rev. Lett. 98, 085503 (2007)]

Investing, personal finance and financial planning

Renewable energy and energy savings

Other modeling, algorithms and computations

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