Stochastic Collocation Method Research Articles

Ensemble Time-Stepping Algorithm for the Convection-Diffusion Equation with Random Diffusivity

In this paper, we develop two ensemble time-stepping algorithms to solve the convection-diffusion equation with random diffusion coefficients, forcing terms and initial conditions based on the pseudo-spectral stochastic collocation method. The key step of the pseudo-spectral stochastic collocation method is to solve a number of deterministic problems derived from the original stochastic convection-diffusion equation. In general, a common way to solve the set of deterministic problems is by using the backward differentiation formula, which requires us to store the coefficient matrix and right-hand-side vector multiple times, and solve them one by one. However, the proposed algorithm only need to solve a single linear system with one shared coefficient matrix and multiple right-hand-side vectors, reducing both storage required and computational cost of the solution process. The stability and error analysis of the first- and second-order ensemble time-stepping algorithms are provided. Several numerical experiments are presented to confirm the theoretical analyses and verify the feasibility and effectiveness of the proposed method.

A probability‐box approach on uncertain correlation lengths by stochastic finite element method

AbstractIn order to regard mixed aleatory and epistemically uncertain random fields within stochastic finite element method, a probability box approach using stochastic collocation method is introduced. The influence of an interval‐valued correlation length on the output is investigated.

Variance‐based simplex stochastic collocation with model order reduction for high‐dimensional systems

SummaryIn this work, an adaptive simplex stochastic collocation method is introduced in which sample refinement is informed by variability in the solution of the system. The proposed method is based on the concept of multi‐element stochastic collocation methods and is capable of dealing with very high‐dimensional models whose solutions are expressed as a vector, a matrix, or a tensor. The method leverages random samples to create a multi‐element polynomial chaos surrogate model that incorporates local anisotropy in the refinement, informed by the variance of the estimated solution. This feature makes it beneficial for strongly nonlinear and/or discontinuous problems with correlated non‐Gaussian uncertainties. To solve large systems, a reduced‐order model (ROM) of the high‐dimensional response is identified using singular value decomposition (higher‐order SVD for matrix/tensor solutions) and polynomial chaos is used to interpolate the ROM. The method is applied to several stochastic systems of varying type of response (scalar/vector/matrix) and it shows considerable improvement in performance compared to existing simplex stochastic collocation methods and adaptive sparse grid collocation methods.

Nanoelectronic COupled Problems Solutions: uncertainty quantification for analysis and optimization of an RFIC interference problem

The FP7 project nanoCOPS (the 7th Framework Programme project Nanoelectronic COupled Problems Solutions) derived new methods for simulation during development of designs of integrated products. It has covered advanced simulation techniques for electromagnetics with feedback couplings to electronic circuits, heat and stress. It was inspired by interest from semiconductor industry and by a simulation tool vendor in electronic design automation.Due to the application of higher frequencies and the continuous down-scaling process, there is a higher probability of unforeseen interactions between different domains of a Radio Frequency Integrated Circuit (RFIC), which can lead to the variability of the output performance functions. Since these undesired phenomena ought to be investigated in the early phases of the integrated circuit (IC) design, in this work we formulate the robust optimization problem in terms of the expectation and the standard deviation values under the uncertainties of material parameters.Therein, the statistical information included in the multi-objective functional can be provided by a response surface model. For this purpose the Stochastic Collocation Method (SCM) combined with Polynomial Chaos Expansion (PCE) has been used. The reason for analyzing the variability of the Electromagnetic Interference (EMI) is, on the one hand, to quantify the uncertainty in an integrated Radio-Frequency Complementary Metal-Oxide Semi-Conductor (RFCMOS) transceiver design, and, on the other hand, to improve this design in a robust sense. We have illustrated our methodology for an integrated Radio-Frequency Complementary Metal-Oxide Semi-Conductor (RFCMOS) transceiver design.

Estimation of exciton diffusion lengths of organic semiconductors in random domains

Exciton diffusion length plays a vital role in the function of opto-electronic devices. In many experiments, photoluminescence in a regular domain is measured as the observation data to estimate this length parameter in an inverse manner. However, the domain occupied by the organic semiconductor is often subject to surface measurement error. The result is sometimes sensitive to the surface geometry of the domain and the estimations based on 1D or 2D models are found to be inconsistent due to the uncertainty in the domain boundary. In this paper, we employ a random function representation to address this uncertainty. Our forward model is a diffusion-type equation over the domain whose geometric boundary is subject to small random perturbations. We propose an asymptotic-based method as an approximate forward solver which only needs to solve several deterministic problems over a fixed domain. For the same accuracy requirements we tested here, the running time of our approach is more than one order of magnitude smaller than that of directly solving the original stochastic problem by the stochastic collocation method. From numerical results, we find that the correlation length of randomness is important to determine whether a 1D reduced model is a good surrogate for the 2D model. This discovery suggests that for some materials where both small molecules and polymers can form crystal structures, exciton diffusion can be well described by the 1D model, but for other organic materials with low crystalline order, the reduced 1D model is not sufficiently accurate for diffusion length estimation.

Large-scale stochastic topology optimization using adaptive mesh refinement and coarsening through a two-level parallelization scheme

Topology optimization under uncertainty of large-scale continuum structures is a computational challenge due to the combination of large finite element models and uncertainty propagation methods. The former aims to address the ever-increasing complexity of more and more realistic models, whereas the latter is required to estimate the statistical metrics of the formulation. In this work, the computational burden of the problem is addressed using a sparse grid stochastic collocation method, to calculate the statistical metrics of the topology optimization under uncertainty formulation, and a parallel adaptive mesh refinement method, to efficiently solve each of the stochastic collocation nodes. A two-level parallel processing scheme (TOUU-PS2) is proposed to profit from parallel computation on distributed memory systems: the stochastic nodes are distributed through the distributed memory system, and the efficient computation of each stochastic node is performed partitioning the problem using a domain decomposition strategy and solving each subdomain using an adaptive mesh refinement method. A dynamic load-balancing strategy is used to balance the workload between subdomains, and thus increasing the parallel performance by reducing processor idle time. The topology optimization problem is addressed using the topological derivative concept in combination with a level-set method. The performance and scalability of the proposed methodology are evaluated using several numerical benchmarks and real-world applications, showing good performance and scalability up to thousands of processors.

Structural reliability and stochastic finite element methods

PurposeThis paper aims to provide a comprehensive review of uncertainty quantification methods supported by evidence-based comparison studies. Uncertainties are widely encountered in engineering practice, arising from such diverse sources as heterogeneity of materials, variability in measurement, lack of data and ambiguity in knowledge. Academia and industries have long been researching for uncertainty quantification (UQ) methods to quantitatively account for the effects of various input uncertainties on the system response. Despite the rich literature of relevant research, UQ is not an easy subject for novice researchers/practitioners, where many different methods and techniques coexist with inconsistent input/output requirements and analysis schemes.Design/methodology/approachThis confusing status significantly hampers the research progress and practical application of UQ methods in engineering. In the context of engineering analysis, the research efforts of UQ are most focused in two largely separate research fields: structural reliability analysis (SRA) and stochastic finite element method (SFEM). This paper provides a state-of-the-art review of SRA and SFEM, covering both technology and application aspects. Moreover, unlike standard survey papers that focus primarily on description and explanation, a thorough and rigorous comparative study is performed to test all UQ methods reviewed in the paper on a common set of reprehensive examples.FindingsOver 20 uncertainty quantification methods in the fields of structural reliability analysis and stochastic finite element methods are reviewed and rigorously tested on carefully designed numerical examples. They include FORM/SORM, importance sampling, subset simulation, response surface method, surrogate methods, polynomial chaos expansion, perturbation method, stochastic collocation method, etc. The review and comparison tests comment and conclude not only on accuracy and efficiency of each method but also their applicability in different types of uncertainty propagation problems.Originality/valueThe research fields of structural reliability analysis and stochastic finite element methods have largely been developed separately, although both tackle uncertainty quantification in engineering problems. For the first time, all major uncertainty quantification methods in both fields are reviewed and rigorously tested on a common set of examples. Critical opinions and concluding remarks are drawn from the rigorous comparative study, providing objective evidence-based information for further research and practical applications.

Numerical Investigation of Trajectory and Attitude Robustness of an Underwater Vehicle Considering the Uncertainty of Platform Velocity and Yaw Angle

The vertically launched underwater vehicle always suffers various hydrodynamic disturbances in its water-emerging process due to the uncertainty of the launch platform motion. Based on the nested sparse grid based stochastic collocation method (NSSCM) and nonintrusive polynomial chaos method, the effect of uncertainty of platform velocity and yaw angle on robustness of vehicle's trajectory and attitude is numerically studied. Results indicate that the uncertainty stemming from platform motion propagates along vehicle's water-emerging process. As the negative horizontal velocity of vehicle gradually changes to positive direction, the uncertainty bar of horizontal velocity presents contracting-expanding mode with an “hourglass” shape while the uncertainty bar of horizontal displacement experiences a “spindle-shaped” one (expanding-contracting-expanding), which is a half cycle later compared with the velocity. The uncertain motion of platform enlarges the uncertainty bar of bottom force via its impact on the gas-leakage process of trail bubble, resulting in the increasing of uncertainty of vertical velocity. Pitching angle (attitude of vehicle) and pitching angular velocity of vehicle persist getting worse driven by the pressure difference between vehicle's front and back sides especially on head part. And their continuous increasing uncertainty bars are formed mainly due to the condition that pressure uncertainty of front side is larger than that on back side, which also leads to the increasing of uncertainty of horizontal force.

A two-stage ensemble Kalman filter based on multiscale model reduction for inverse problems in time fractional diffusion-wave equations

Ensemble Kalman filter (EnKF) has been widely used in state estimation and parameter estimation for the dynamic system where observational data is obtained sequentially in time. Very burdened simulations for the forward problem are needed to update a large number of EnKF ensemble samples. This will slow down the analysis efficiency of the EnKF for large-scale and high dimensional models. To reduce uncertainty and accelerate posterior inference, a two-stage ensemble Kalman filter is presented to improve the sequential analysis of EnKF. It is known that the final posterior ensemble may be concentrated in a small portion of the entire support of the initial prior ensemble. It will be much more efficient if we first build a new prior by some partial observations, and construct a surrogate only over the significant region of the new prior. To this end, we construct a very coarse model using generalized multiscale finite element method (GMsFEM) and generate a new prior ensemble in the first stage. GMsFEM provides a set of hierarchical multiscale basis functions supported in coarse blocks. This gives flexibility and adaptivity to choosing degree of freedoms to construct a reduce model. In the second stage, we build an initial surrogate model based on the new prior by using GMsFEM and sparse generalized polynomial chaos (gPC)-based stochastic collocation methods. To improve the initial surrogate model, we dynamically update the surrogate model, which is adapted to the sequential availability of data and the updated analysis. The two-stage EnKF can achieve a better estimation than standard EnKF, and significantly improve the efficiency to update the ensemble analysis (posterior exploration). To enhance the applicability and flexibility in Bayesian inverse problems, we extend the two-stage EnKF to non-Gaussian models and hierarchical models. In the paper, we focus on the time fractional diffusion-wave models in porous media and investigate their Bayesian inverse problems using the proposed two-stage EnKF. A few numerical examples are carried out to demonstrate the performance of the two-stage EnKF method by taking account of parameter and structure inversion in permeability fields and source functions.

Uncertainty quantification and Heston model

In this paper, we study the impact of the parameters involved in Heston model by means of Uncertainty Quantification. The Stochastic Collocation Method already used for example in computational fluid dynamics, has been applied throughout this work in order to compute the propagation of the uncertainty from the parameters of the model to the output. The well-known Heston model is considered and involved parameters in the Feller condition are taken as uncertain due to their important influence on the output. Numerical results where the Feller condition is satisfied or not are shown as well as a numerical example with real market data.

Stochastic collocation approach with adaptive mesh refinement for parametric uncertainty analysis

The presence of a high-dimensional stochastic input domain with discontinuities poses major computational challenges in analyzing and quantifying the effects of the uncertainties in a physical system. In this paper, we propose a stochastic collocation method with adaptive mesh refinement (SCAMR) to deal with high dimensional stochastic systems with discontinuities. Specifically, the proposed approach uses generalized polynomial chaos (gPC) expansion with Legendre polynomial basis and solves for the gPC coefficients using the least squares method. It also implements an adaptive mesh (element) refinement strategy which checks for abrupt variations in the output based on a low-order gPC approximation error to track discontinuities or non-smoothness. In addition, the proposed method involves a criterion for checking possible dimensionality reduction and consequently, the decomposition of the original high-dimensional problem to a number of lower-dimensional subproblems. Specifically, this criterion checks all the existing interactions between input parameters of a specific problem based on the high-dimensional model representation (HDMR) method, and therefore automatically provides the subproblems which only involve interacting input parameters. The efficiency of the approach is demonstrated using examples of both smooth and non-smooth problems with number of input parameters up to 500, and the approach is compared against other existing algorithms.

A Sparse Grid Stochastic Collocation and Finite Volume Element Method for Constrained Optimal Control Problem Governed by Random Elliptic Equations

A Sparse Grid Stochastic Collocation and Finite Volume Element Method for Constrained Optimal Control Problem Governed by Random Elliptic Equations

Evolutionary topology optimization of continuum structures under uncertainty using sensitivity analysis and smooth boundary representation

This paper presents an evolutionary approach for the Robust Topology Optimization (RTO) of continuum structures under loading and material uncertainties. The method is based on an optimality criterion obtained from the stochastic linear elasticity problem in its weak form. The smooth structural topology is determined implicitly by an iso-value of the optimality criterion field. This iso-value is updated using an iterative approach to reach the solution of the RTO problem. The proposal permits to model the uncertainty using random variables with different probability distributions as well as random fields. The computational burden, due to the high dimension of the random field approximation, is efficiently addressed using anisotropic sparse grid stochastic collocation methods. The numerical results show the ability of the proposal to provide smooth and clearly defined structural boundaries. Such results also show that the method provides structural designs satisfying a trade-off between conflicting objectives in the RTO problem.

Variance-Based Robust Optimization of a Permanent Magnet Synchronous Machine

This paper focuses on the application of the variance-based global sensitivity analysis for a topology derivative method in order to solve a stochastic nonlinear time-dependent magnetoquasistatic interface problem. To illustrate the approach a permanent magnet synchronous machine has been considered. Our key objective is to provide a robust design of the rotor poles and of the tooth base in a stator for the reduction of the torque ripple and electromagnetic losses, while taking material uncertainties into account. Input variations of material parameters are modeled using the polynomial chaos expansion technique, which is incorporated into the stochastic collocation method in order to provide a response surface model. Additionally, we can benefit from the variance-based sensitivity analysis. This allows us to reduce the dimensionality of the stochastic optimization problems, described by the random-dependent cost functional. Finally, to validate our approach, we provide the two-dimensional simulations and analysis, which confirm the usefulness of the proposed method and yield a novel topology of a permanent magnet synchronous machine.

Stochastic process design kits for photonic circuits based on polynomial chaos augmented macro-modelling

Fabrication tolerances can significantly degrade the performance of fabricated photonic circuits and process yield. It is essential to include these stochastic uncertainties in the design phase in order to predict the statistical behaviour of a device before the final fabrication. This paper presents a method to build a novel class of stochastic-based building blocks for the preparation of Process Design Kits for the analysis and design of photonic circuits. The proposed design kits directly store the information on the stochastic behaviour of each building block in the form of a generalized-polynomial-chaos-based augmented macro-model obtained by properly exploiting stochastic collocation and Galerkin methods. Using these macro-models, only a single deterministic simulation is required to compute the stochastic moments of any arbitrary photonic circuit, without the need of running a large number of time-consuming circuit simulations thereby dramatically improving simulation efficiency. The effectiveness of the proposed approach is verified by means of classical photonic circuit examples with multiple uncertain variables.

Regularity analysis of metamaterial Maxwell’s equations with random coefficients and initial conditions

In this paper we develop and analyze the stochastic collocation method for solving the time-dependent metamaterial Maxwell’s equations subject to random coefficients and random initial conditions. We provide a rigorous regularity analysis of the solution with respect to the random variables. To our best knowledge, this is the first theoretical results derived for the stochastic metamaterial Maxwell equations. The rate of convergence is proved depending on the regularity of the solution. Numerical results are presented to confirm the theoretical analysis. We also demonstrate that the backward wave propagation phenomenon still exists in random metamaterial.

Dimension-Reduced Sparse Grid Strategy for a Stochastic Collocation Method in EMC Software

Stochastic collocation method (SCM), a prevailing uncertainty analysis method, has been successfully implemented in electromagnetic compatibility (EMC) simulation, especially in EMC commercial software. However, the “curse of dimensionality” problem (dimensionality means the number of uncertain variables) limits the application of the SCM. This paper proposes a novel sparse grid strategy in order to improve the computational efficiency of the SCM, especially in high-dimensionality case. In the proposed strategy, it is revealed that the number of the collocation points is in proportion to the dimensionality. By simulating two shielding effectiveness analysis examples in CST software, the feasibility of the proposed method can be presented clearly, with the help of the feature selective validation method.

Robust Optimal Design of Quantum Electronic Devices

We consider the optimal design of a sequence of quantum barriers, in order to manufacture an electronic device at the nanoscale such that the dependence of its transmission coefficient on the bias voltage is linear. The technique presented here is easily adaptable to other response characteristics. There are two distinguishing features of our approach. First, the transmission coefficient is determined using a semiclassical approximation, so we can explicitly compute the gradient of the objective function. Second, in contrast with earlier treatments, manufacturing uncertainties are incorporated in the model through random variables; the optimal design problem is formulated in a probabilistic setting and then solved using a stochastic collocation method. As a measure of robustness, a weighted sum of the expectation and the variance of a least-squares performance metric is considered. Several simulations illustrate the proposed technique, which shows an improvement in accuracy over 69% with respect to brute-force, Monte-Carlo-based methods.

Collocation Methods for Exploring Perturbations in Linear Stability Analysis

Eigenvalue analysis is a well-established tool for stability analysis of dynamical systems. However, there are situations where eigenvalues miss some important features of physical models. For example, in models of incompressible fluid dynamics, there are examples where linear stability analysis predicts stability but transient simulations exhibit significant growth of infinitesimal perturbations. This behavior can be predicted by pseudo-spectral analysis. In this study, we show that an approach similar to pseudo-spectral analysis can be performed inexpensively using stochastic collocation methods and the results can be used to provide quantitative information about instability. In addition, we demonstrate that the results of the perturbation analysis provide insight into the behavior of unsteady flow simulations.

Stochastic Collocation Applications in Computational Electromagnetics

The paper reviews the application of deterministic-stochastic models in some areas of computational electromagnetics. Namely, in certain problems there is an uncertainty in the input data set as some properties of a system are partly or entirely unknown. Thus, a simple stochastic collocation (SC) method is used to determine relevant statistics about given responses. The SC approach also provides the assessment of related confidence intervals in the set of calculated numerical results. The expansion of statistical output in terms of mean and variance over a polynomial basis, via SC method, is shown to be robust and efficient approach providing a satisfactory convergence rate. This review paper provides certain computational examples from the previous work by the authors illustrating successful application of SC technique in the areas of ground penetrating radar (GPR), human exposure to electromagnetic fields, and buried lines and grounding systems.