PurposeMetamodeling is an effective method to approximate the relations between input and output parameters when significant efforts of experiments and simulations are required to collect the data to build the relations. This paper aims to develop a new sequential sampling method for adaptive metamodeling by using the data with highly nonlinear relation between input and output parameters.Design/methodology/approachIn this method, the Latin hypercube sampling method is used to sample the initial data, and kriging method is used to construct the metamodel. In this work, input parameter values for collecting the next output data to update the currently achieved metamodel are determined based on qualities of data in both the input and output parameter spaces. Uniformity is used to evaluate data in the input parameter space. Leave-one-out errors and sensitivities are considered to evaluate data in the output parameter space.FindingsThis new method has been compared with the existing methods to demonstrate its effectiveness in approximation. This new method has also been compared with the existing methods in solving global optimization problems. An engineering case is used at last to verify the method further.Originality/valueThis paper provides an effective sequential sampling method for adaptive metamodeling to approximate highly nonlinear relations between input and output parameters.

This paper presents a novel method to generate three-axis CNC tool paths for machining freeform surfaces based on a preferred feed direction field. This research was initiated from a fluid dynamics behavior that the energy loss can be reduced when the streamlines of fluid and the small grooves on a surface are in the same directions. In this research, the fluid streamlines above the surface are defined by a collection of vectors. These vectors are regularized into a grid of vectors, and these regularized vectors are further projected onto the tangent planes of a grid of points on the surface to create the preferred feed direction field. Based on the parametric model of the surface, the vectors on the tangent planes of the surface are mapped into vectors in the parametric domain. A scalar function is constructed such that the isolines of this scalar function and the preferred feed direction vectors in the parametric domain are in the same directions. A group of isolines of the scalar function are identified and these isolines are mapped back onto the 3-D surface as the created tool paths considering the tolerance requirement. The developed method has been applied to generate the tool paths for machining surfaces of a compressor blade. © 2019, Korean Society for Precision Engineering.

Financial price series trend prediction is an essential problem which has been discussed extensively using tools and techniques of economic physics and machine learning. Time dependence and volatility issues in this problem have made Hidden Markov Model (HMM) a useful tool in predicting the states of stock market. In this paper, we present an approach to predict the stock market price trend based on high-order HMM. Different from the commonly used first-order HMM, short and long-term time dependence are both considered in the high order HMM. By introducing a dimension reduction method which could transform the high-dimensional state vector of high-order HMM into a single one, we present a dynamic high-order HMM trading strategy to predict and trade CSI 300 and S&P 500 stock index for the next day given historical data. In our approach, we make a statistic of the daily returns in the history to demonstrate the relationship between hidden states and the price change trend. Experiments on CSI 300 and S&P 500 index illustrate that high-order HMM has preferable ability to identify market price trend than first-order one. Thus, the high-order HMM has higher accuracy and lower risk than the first-order model in predicting the index price trend. © 2018 Elsevier B.V.

Understanding the correlations among stock returns is crucial for reducing the risk of investment in stock markets. As an important stylized correlation, lead–lag effect plays a major role in analyzing market volatility and deriving trading strategies. Here, we explore historical lead–lag relationships among stocks in the Chinese stock market. Strongly positive lagged correlations can be empirically observed. We demonstrate this lead–lag phenomenon is not constant but temporally emerges during certain periods. By introducing moving time window method, we transform the lead–lag dynamics into a series of asymmetric lagged correlation matrices. Dynamic lead–lag structures are uncovered in the form of temporal network structures. We find that the size of lead–lag group experienced a rapid drop during the year 2012, which signaled a re-balance of the stock market. On the daily timescale, we find the lead–lag structure exhibits several persistent patterns, which can be characterized by the Jaccard matrix. We show significant market events can be distinguished in the Jaccard matrix diagram. Taken together, we study an integration of all the temporal networks and identify several leading stock sectors, which are in accordance with the common Chinese economic fundamentals. © 2018 Elsevier B.V.

As the extensive use of first-principles Density Functional Theory (DFT) simulations, using DFT for high-throughput screening to predict the desirable doped structures that are physically stable with optimal properties becomes common. Usually, the challenge of running doping calculation is how to obtain inequivalent doped structures as input for DFT simulations to find the desirable doped structure(s). The current practice of substitutional doping is mainly based on experience to use one or more dopant atoms to replace target atoms to be substituted. Using this manual approach to produce all inequivalent doped structures based on expertise is tedious, and the results are usually incomplete. To address this need, we propose a "doping-filtering" collaboratively working approach and develop associated high-throughput computational algorithms to obtain inequivalent doped structures for substitutional doping-based high-throughput screening effectively. A computational time benchmark matrix table of using this approach to obtain inequivalent doped structures for different doping concentrations is also given. The algorithm is integrated into a high-throughput computational material infrastructure named MatCloud. It has been demonstrated in the study case of doping Ni, Co, Ti and Sc into Zr2Fe that the approach and algorithm are effective in reducing the computational time in obtaining inequivalent doped structures. © 2018 Elsevier B.V.

MatCloud provides a high-throughput computational materials infrastructure for the integrated management of materials simulation, data, and computing resources. In comparison to AFLOW, Material Project, and NoMad, MatCloud delivers two-fold functionalities: a computational materials platform where users can do on-line job setup, job submission and monitoring only via Web browser, and a materials properties simulation database. It is developed under Chinese Materials Genome Initiative and is a China own proprietary high-throughput computational materials infrastructure. MatCloud has been on line for about one year, receiving considerable registered users, feedbacks, and encouragements. Many users provided valuable input and requirements to MatCloud. In this paper, we describe the present MatCloud, future visions, and major challenges. Based on what we have achieved, we will endeavour to further develop MatCloud in an open and collaborative manner and make MatCloud a world known China-developed novel software in the pressing area of high-throughput materials calculations and materials properties simulation database within Material Genome Initiative. © 2018 Institute of Physics Publishing.All right reserved.

Pore-scale simulation is an essential tool to understand complex physical process in many environmental problems. However, structural heterogeneity and data scarcity render the porous medium, and in turn its macroscopic properties, uncertain. Meanwhile, direct numerical simulation of the medium at the fine scale often incurs high computational cost, which further limits efforts to quantify the parametric uncertainty over those macroscopic properties. To address this challenge, we propose a framework to compute the probabilistic density function (PDF) of the macroscopic property based on the generalized polynomial chaos expansion method and the Minkowski functionals. To illustrate the effectiveness of our approach, we conduct numerical experiments for one macroscopic property, namely the permeability, and we compare its PDF with that obtained from Monte Carlo simulations. Both two- and three-dimensional cases show that our framework requires much fewer realizations while maintaining the desired accuracy. © 2018 American Physical Society.

Complex systems are often described with competing models. Such divergence of interpretation on the system may stem from model fidelity, mathematical simplicity, and more generally, our limited knowledge of the underlying processes. Meanwhile, available but limited observations of system state could further complicate one's prediction choices. Over the years, data assimilation techniques, such as the Kalman filter, have become essential tools for improved system estimation by incorporating both models forecast and measurement; but its potential to mitigate the impacts of aforementioned model-form uncertainty has yet to be developed. Based on an earlier study of Multi-model Kalman filter, we propose a novel framework to assimilate multiple models with observation data for nonlinear systems, using extended Kalman filter, ensemble Kalman filter and particle filter, respectively. Through numerical examples of subsurface flow, we demonstrate that the new assimilation framework provides an effective and improved forecast of system behavior. © 2017 Elsevier Inc.