Lectures related to Equilibrium GLE sampling

Explores combining GLEs and PIMD for fast, accurate simulations in atomistic modeling.

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

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.

Generalized Langevin Equations

Explores Generalized Langevin Equations and their computational implications in molecular dynamics simulations, emphasizing the impact of noise details on particle trajectories.

Graph Coloring: Theory and Applications

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Recovering dynamical properties: Path Integral Methods

Explores deconvolution of GLE spectra to predict and extract dynamical properties from MD.

Efficient Stochastic Numerical Methods

Explores efficient stochastic numerical methods for modeling and learning, covering topics like the Analytical Engine and kinase inhibitors.

Langevin dynamics: Path Integral Methods

Covers Langevin dynamics, Fokker-Planck equation, solving the Langevin equation, and efficiency of Langevin sampling in molecular dynamics.

Modeling Biomolecular Simulations and Experiments

Explores the evolution of biomolecular simulations, emphasizing accurate models, increased sampling, and the transformative role of simulations in predicting experimental outcomes.

Atomistic Computer Modelling of Materials: Simulating and Sampling

Covers simulating and sampling in atomistic computer modelling of materials.

Uncertainty Quantification for Coarse-Grained Molecular Dynamics

Delves into uncertainty quantification in molecular dynamics, focusing on aerospace materials, modeling decisions, and coarse-grained dynamics.

Deep Generative Models in Drug Discovery

Explores the application of deep generative models in drug discovery, focusing on designing small molecules and optimizing molecular structures.

Estimation Methods: BLP and Control-Function

Covers the inconsistency in estimates due to endogeneity and introduces the BLP and Control-Function estimation methods.

Path Integral Methods: Advanced Techniques

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

Markov Chain Monte Carlo: Sampling and Convergence

Explores Markov Chain Monte Carlo for sampling high-dimensional distributions and optimizing functions using the Metropolis-Hastings algorithm.

Determinantal Point Processes and Extrapolation

Covers determinantal point processes, sine-process, and their extrapolation in different spaces.

Degree of Freedom Analysis

Explores degree of freedom analysis, redundancy, and data reconciliation in process modeling and optimization.

Generative Models: Advancements in Molecular Design

Explores the use of generative models for discovering novel molecules in molecular design.