Modeling Biomolecular Simulations and Experiments

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Related lectures (31)

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Combining GLEs and PIMD

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

Sampling: Signal Reconstruction and Aliasing

Covers the importance of sampling, signal reconstruction, and aliasing in digital representation.

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.

Signals & Systems I: Sampling and Reconstruction

Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.

Sampling: DT-time processing of CT signals

Covers the importance of sampling in signal processing, including the sampling theorem and signal reconstruction.

Theory of MCMC

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

Introduction to Sampling

Covers the concept of sampling, the sampling theorem, signal reconstruction, and the conversion of analogue signals to digital signals.

Fourier Transform and Sampling

Covers the Fourier transform of sampled signals, reconstruction, and harmonic response.

Molecular Dynamics Basics

Covers the fundamentals of Molecular Dynamics, including the Ergodic Hypothesis, force fields, and periodic boundary conditions.