Lectures related to Efficient Stochastic Numerical Methods

Explicit Stabilised Methods: Applications to Bayesian Inverse Problems

Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.

Molecular dynamics under constraints

Explores molecular dynamics simulations under holonomic constraints, focusing on numerical integration and algorithm formulation.

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Scientific Computing in Neuroscience

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

Monte Carlo Techniques: Sampling and Simulation

Explores Monte Carlo techniques for sampling and simulation, covering integration, importance sampling, ergodicity, equilibration, and Metropolis acceptance.

Turbulence: Numerical Flow Simulation

Explores turbulence characteristics, simulation methods, and modeling challenges, providing guidelines for choosing and validating turbulence models.

Computational Science and Engineering: Master Program Overview

Outlines the Master in Computational Science and Engineering program at EPFL, detailing its structure, projects, and career opportunities for graduates.

Density of States and Bayesian Inference in Computational Mathematics

Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.

Molecular dynamics and integrators

Explores molecular dynamics, integrators, trajectory generation, and error monitoring in atomistic modeling.

Path Integral Methods: Advanced Techniques

Explores advanced path integral methods in computational science, covering efficient sampling, colored noise, high-order integrals, and quantum thermostats.

Natural Language Generation: Decoding & Training

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Theory of MCMC

Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.

Normal Distribution: Characteristics and Examples

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Learning Chemical Reaction Networks

Explores sparse learning of chemical reaction networks from trajectory data using data-based methods and learning approaches.

Path Integral Methods: Convergence and Computational Techniques

Covers path integral methods, focusing on convergence and computational techniques for quantum systems.

Mechanics of Slender Structure: Naturally Curly Hair in 3D

Delves into the mechanics of slender structures using naturally curly hair in 3D as an example.

Sampling the Canonical Ensemble

Explores sampling the canonical ensemble, temperature fluctuations, extended Lagrangian, and Maxwell-Boltzmann distribution in molecular dynamics simulations.

Molecular Dynamics and Monte Carlo

Covers computational methods for molecular systems at finite temperature, emphasizing stochastic sampling and time evolution simulations.

Monte Carlo Methods: Thermal Radiation Applications

Discusses the application of Monte Carlo methods in thermal radiation analysis, focusing on probability functions and numerical integration techniques.

Probabilities and Statistics: Key Theorems and Applications

Discusses key statistical concepts, including sampling dangers, inequalities, and the Central Limit Theorem, with practical examples and applications.