Bayesian uncertainty quantification and propagation in molecular dynamics simulations: a high performance computing framework.
نویسندگان
چکیده
We present a Bayesian probabilistic framework for quantifying and propagating the uncertainties in the parameters of force fields employed in molecular dynamics (MD) simulations. We propose a highly parallel implementation of the transitional Markov chain Monte Carlo for populating the posterior probability distribution of the MD force-field parameters. Efficient scheduling algorithms are proposed to handle the MD model runs and to distribute the computations in clusters with heterogeneous architectures. Furthermore, adaptive surrogate models are proposed in order to reduce the computational cost associated with the large number of MD model runs. The effectiveness and computational efficiency of the proposed Bayesian framework is demonstrated in MD simulations of liquid and gaseous argon.
منابع مشابه
A New Hybrid Framework for Filter based Feature Selection using Information Gain and Symmetric Uncertainty (TECHNICAL NOTE)
Feature selection is a pre-processing technique used for eliminating the irrelevant and redundant features which results in enhancing the performance of the classifiers. When a dataset contains more irrelevant and redundant features, it fails to increase the accuracy and also reduces the performance of the classifiers. To avoid them, this paper presents a new hybrid feature selection method usi...
متن کاملRealizing Exascale Performance for Uncertainty Quantification
Motivation Exascale computing promises to address many scientific and engineering problems of national interest by facilitating computational simulation of physical phenomena at tremendous new levels of accuracy, fidelity, and scale, as well as unprecedented capabilities for high-level analysis such as uncertainty quantification for today’s petascale computational simulations. Uncertainty quant...
متن کاملUncertainty Reduction using Bayesian Inference and Sensitivity Analysis: A Sequential Approach to the NASA Langley Uncertainty Quantification Challenge
This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic uncertainty, and develops a rigorous methodology for efficient refinement of epistemic uncertainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The propose...
متن کاملBayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
We are interested in the development of surrogate models for uncertainty quantification and propagation in problems governed by stochastic PDEs using a deep convolutional encoder-decoder network in a similar fashion to approaches considered in deep learning for image-to-image regression tasks. Since normal neural networks are data intensive and cannot provide predictive uncertainty, we propose ...
متن کاملMechanical Characteristics and Failure Mechanism of Nano-Single Crystal Aluminum Based on Molecular Dynamics Simulations: Strain Rate and Temperature Effects
Besides experimental methods, numerical simulations bring benefits and great opportunities to characterize and predict mechanical behaviors of materials especially at nanoscale. In this study, a nano-single crystal aluminum (Al) as a typical face centered cubic (FCC) metal was modeled based on molecular dynamics (MD) method and by applying tensile and compressive strain loadings its mechanical ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- The Journal of chemical physics
دوره 137 14 شماره
صفحات -
تاریخ انتشار 2012