Samuel Herrmann

Samuel HERRMANN

Professor of Applied Mathematics at the Université Bourgogne Europe
Director of the Bourgogne Franche-Comté Mathematics Federation
Member of the Statistics, Probability, Control and Optimization team (SPOC)

Research

Main topic: My research framework concerns the analysis of stochastic dfferential equations.
1. Asymptotic analysis related to the study of certain stochastic processes. This research area includes the study of large deviation phenomena for nonlinear stochastic processes such as self-attracting diffusions (driven by their own law), the analysis of diffusions perturbed by a periodic signal (highlighting stochastic resonance), long-time behavior of long-memory diffusions, and the study of chaos propagation in large interacting particle systems (solutions of stochastic differential equations). These studies are connected to problems arising from climate science, biology, and financial modeling.
2. Stochastic algorithms (simulation of passage time or exit time for diffusion processes).

Overview

PhD in 2001 (University of Nancy 1, under Bernard Roynette): Study of diffusion processes.
HDR in 2009: Asymptotic analysis related to certain stochastic processes

Academic positions :

Published articles

Other research articles

Book

Dissertations

Teaching (2024–2025)

Simulation of stochastic processes (Master in Turin)


  1. Chapter 1: Simulation of random variables
  2. 1. Random number generators
    2. Classical discrete random variables
    3. Continuous random variables (reciprocal function and acceptance/rejection methods)

    Exercises and Python file (jupyter notebook)

  3. Chapter 2: Monte Carlo methods
  4. 1. Rejection method (Hit or miss)
    2. Sample mean method
    3. Variance reduction techniques

    Exercises and Python file (jupyter notebook)

  5. Chapter 3: Simulation of a Brownian motion
  6. 1. Properties and definition
    2. Simulation of 1-dimensional binomial
    - As a Gaussian processes
    - Using Lévy's argument (Brownian bridge)
    - Karhunen-Loève theorem
    3. Simulation of a d-dimensional Brownian motion, a Q-Brownian motion

    Exercises and Python file (jupyter notebook)

  7. Chapter 4: Stochastic differential equations (SDE) - définition and simulation
  8. 1. Introduction: deterministic IVP
    2. Stochastic integration
    3. Euler and Milstein schemes
    4. Exact simulation method
    Python file (jupyter notebook)