Foundations of Dissipative Particle Dynamics

We derive a mesoscopic modeling and simulation technique that is very close to the technique known as dissipative particle dynamics. The model is derived from molecular dynamics by means of a systematic coarse-graining procedure. Thus the rules governing our new form of dissipative particle dynamics reflect the underlying molecular dynamics; in particular all the underlying conservation laws carry over from the microscopic to the mesoscopic descriptions. Whereas previously the dissipative particles were spheres of fixed size and mass, now they are defined as cells on a Voronoi lattice with variable masses and sizes. This Voronoi lattice arises naturally from the coarse-graining procedure which may be applied iteratively and thus represents a form of renormalisation-group mapping. It enables us to select any desired local scale for the mesoscopic description of a given problem. Indeed, the method may be used to deal with situations in which several different length scales are simultaneously present. Simulations carried out with the present scheme show good agreement with theoretical predictions for the equilibrium behavior.Comment: 18 pages, 7 figure

Ensembles are required to handle aleatoric and parametric uncertainty in molecular dynamics simulation

Classical molecular dynamics is a computer simulation technique that is in widespread use across many areas of science, from physics and chemistry to materials, biology, and medicine. The method continues to attract criticism due its oft-reported lac

EasyVVUQ: A library for verification, validation and uncertainty quantification in high performance computing

EasyVVUQ is an open source Python library (https://github.com/UCL-CCS/EasyVVUQ) designed to facilitate verification, validation and uncertainty quantification (VVUQ) for a wide variety of simulations. The goal of EasyVVUQ is to make it as easy as possible to implement advanced VVUQ techniques for existing application codes or workflows. Our aim is to expose these features in an accessible way for users of scientific software, in particular for simulation codes running on high performance computers

FabSim3: An automation toolkit for verified simulations using high performance computing

A common feature of computational modelling and simulation research is the need to perform many tasks in complex sequences to achieve a usable result. This will typically involve tasks such as preparing input data, pre-processing, running simulations on a local or remote machine, post-processing, and performing coupling communications, validations and/or optimisations. Tasks like these can involve manual steps which are time and effort intensive, especially when it involves the management of large ensemble runs. Additionally, human errors become more likely and numerous as the research work becomes more complex, increasing the risk of damaging the credibility of simulation results. Automation tools can help ensure the credibility of simulation results by reducing the manual time and effort required to perform these research tasks, by making more rigorous procedures tractable, and by reducing the probability of human error due to a reduced number of manual actions. In addition, efficiency gained through automation can help researchers to perform more research within the budget and effort constraints imposed by their projects. This paper presents the main software release of FabSim3, and explains how our automation toolkit can improve and simplify a range of tasks for researchers and application developers. FabSim3 helps to prepare, submit, execute, retrieve, and analyze simulation workflows. By providing a suitable level of abstraction, FabSim3 reduces the complexity of setting up and managing a large-scale simulation scenario, while still providing transparent access to the underlying layers for effective debugging. The tool also facilitates job submission and management (including staging and curation of files and environments) for a range of different supercomputing environments. Although FabSim3 itself is application-agnostic, it supports a provably extensible plugin system where users automate simulation and analysis workflows for their own application domains. To highlight this, we briefly describe a selection of these plugins and we demonstrate the efficiency of the toolkit in handling large ensemble workflows

Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit

The VECMA toolkit enables automated Verification, Validation and Uncertainty Quantification (VVUQ) for complex applications that can be deployed on emerging exascale platforms and provides support for software applications for any domain of interest. The toolkit has four main components including EasyVVUQ for VVUQ workflows, FabSim3 for automation and tool integration, MUSCLE3 for coupling multiscale models and QCG tools to execute application workflows on high performance computing (HPC). A more recent addition to the VECMAtk is EasySurrogate for various types of surrogate methods. In this paper, we present five tutorials from different application domains that apply these VECMAtk components to perform uncertainty quantification analysis, use surrogate models, couple multiscale models and execute sensitivity analysis on HPC. This paper aims to provide hands-on experience for practitioners aiming to test and contrast with their own applications

Quantifying uncertainties in Direct Numerical Simulations of a turbulent channel flow

Direct numerical simulation (DNS) provides unrivalled levels of detail and accuracy for simulating turbulent flows. However, like all numerical methods, DNS is subject to uncertainties arising from the numerical scheme and input parameters (e.g. mesh resolution). While uncertainty quantification (UQ) techniques are being employed more and more to provide a systematic analysis of uncertainty for lower-fidelity models, their application to DNS is still relatively rare. In light of this, the aim of this work is to apply UQ and sensitivity analysis to the DNS of a canonical wall-bounded turbulent channel flow at low Reynolds number (Reτ=180). To compute the DNS, Incompact3d – a highly scalable open-source framework based on high-order compact finite differences and a spectral Poisson solver – is used as a black-box solver. Stochastic collocation is used to propagate the input uncertainties through Incompact3d to the output quantities of interest (QOIs). To facilitate the non-intrusive forward UQ analysis, the open-source EasyVVUQ package is used to provide integrated capability for sampling, pre-processing, execution, post-processing, and analysis of the computational campaign. Three separate UQ campaigns are conducted. The first two examine the effect of domain size and the numerical parameters (e.g. mesh resolution, time step, sample time), respectively, and adopt Gaussian quadrature rules combined via tensor products to sample the multi-dimensional input space. Finally, the third campaign investigates the performance of a dimension-adaptive sampling strategy that significantly reduces the computational cost compared to the full tensor product approach. The analysis focuses on the cross-channel statistical moments of the QOIs, as well as local and global sensitivity analyses to assess the sensitivity of each QOI with respect to each individual input. This enables an assessment of the robustness and sensitivity of DNS to the user-defined numerical parameters for wall-bounded turbulent flows, and provides an indication of suitable ranges for defining the values of these parameters

Data and scripts for reproducing "Quantifying uncertainties in Direct Numerical Simulations of a turbulent channel flow"

This is the accompanying data and Python scripts to reproduce the figures in "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow", currently under review

Automated variance-based sensitivity analysis of a heterogeneous atomistic-continuum system

A fully automated computational tool for the study of the uncertainty in a mathematical-computational model of a heterogeneous multi-scale atomistic-continuum coupling system is implemented and tested in this project. This tool can facilitate quantitative assessments of the model’s overall uncertainty for a given specific range of variables. The computational approach here is based on the polynomial chaos expansion using projection variance, a pseudo-spectral method. It also supports regression variance, a point collocation method with nested quadrature point where the random sampling method takes a dictionary of the names of the parameters which are manually defined to vary with corresponding distributions. The tool in conjunction with an existing platform for verification, validation, and uncertainty quantification offers a scientific simulation environment and data processing workflows that enables the execution of simulation and analysis tasks on a cluster or supercomputing platform with remote submission capabilities

Global ranking of the sensitivity of interaction potential contributions within classical molecular dynamics force fields

Uncertainty quantification (UQ) is rapidly becoming a sine qua non for all forms of computational science out of which actionable outcomes are anticipated. Much of the microscopic world of atoms and molecules has remained immune to these developments but due to the fundamental problems of reproducibility and reliability, it is essential that practitioners pay attention to the issues concerned. Here a UQ study is undertaken of classical molecular dynamics with a particular focus on uncertainties in the high-dimensional force-field parameters, which affect key quantities of interest, including material properties and binding free energy predictions in drug discovery and personalized medicine. Using scalable UQ methods based on active subspaces that invoke machine learning and Gaussian processes, the sensitivity of the input parameters is ranked. Our analyses reveal that the prediction uncertainty is dominated by a small number of the hundreds of interaction potential parameters within the force fields employed. This ranking highlights what forms of interaction control the prediction uncertainty and enables systematic improvements to be made in future optimizations of such parameters

Mutation V111I in HIV-2 reverse transcriptase increases the fitness of the nucleoside analogue-resistant K65R and Q151M viruses

Infection with HIV-2 can ultimately lead to AIDS, although disease progression is much slower than with HIV-1. HIV-2 patients are mostly treated with a combination of nucleoside reverse transcriptase (RT) inhibitors (NRTIs) and protease inhibitors designed for HIV-1. Many studies have described the development of HIV-1 resistance to NRTIs and identified mutations in the polymerase domain of RT. Recent studies have shown that mutations in the connection and RNase H dom