PC expansion for material parameters using artificial data and statistical methods

This paper investigates the uncertainty of a hyper-elastic model by random material parameters as stochastic variables. For its stochastic discretization a polynomial chaos (PC) is used to expand the coefficients into deterministic and stochastic parts. Then, from experimental data in combination with artificial data for elastomers the distribution of the force-displacement curves are known. In the numerical example the PC-based stochastic and the deterministic parameter identification are used for generation of the distribution of Ogden's material parameters. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Non-linear Stochastic Finite Element

This paper investigates the uncertainty of physically non-linear problems by modeling the elastic random material parameters as stochastic fields. For its stochastic discretization a polynomial chaos (PC) is used to expand the coefficients into deterministic and stochastic parts. Then, from experimental data for an adhesive material the distribution of the random variables, i.e. Young's modulus E(θ), the static yield point Y₀ and the nonlinear hardening parameters q and b, are known. In the numerical example the distribution of the stresses obtained by the PC based SFEM and Monte Carlo simulation is compared. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A chaotic attractor in ecology: theory and experimental data

Chaos has now been documented in a laboratory population. In controlled laboratory experiments, cultures of flour beetles (Tribolium castaneum) undergo bifurcations in their dynamics as demographic parameters are manipulated. These bifurcations, including a specific route to chaos, are predicted by a well-validated deterministic model called the “LPA model”. The LPA model is based on the nonlinear interactions among the life cycle stages of the beetle (larva, pupa and adult). A stochastic version of the model accounts for the deviations of data from the deterministic model and provides the means for parameterization and rigorous statistical validation. The chaotic attractor of the deterministic LPA model and the stationary distribution of the stochastic LPA model describe the experimental data in phase space with striking accuracy. In addition, model-predicted temporal patterns on the attractor are observed in the data. This paper gives a brief account of the interdisciplinary effort that obtained these results.     

Stochastic Material and Topology Design

In order to find a robust optimal topology or material design with respect to stochastic variations of the model parameters of a mechanical structure, the basic optimization problem under stochastic uncertainty must be replaced by an appropriate deterministic substitute problem. Starting from the equilibrium equation and the yield/strength conditions, the problem can be formulated as a stochastic (linear) program “with recourse”. Hence, by discretization the design space by finite elements, linearizing the yield conditions, in case of discrete probability distributions the resulting deterministic substitute problems are linear programs with a dual decomposition data structure.     

Planning logistics operations in the oil industry

In this paper we apply stochastic programming modelling and solution techniques to planning problems for a consortium of oil companies. A multiperiod supply, transformation and distribution scheduling problem—the Depot and Refinery Optimization Problem (DROP)—is formulated for strategic or tactical level planning of the consortium's activities. This deterministic model is used as a basis for implementing a stochastic programming formulation with uncertainty in the product demands and spot supply costs (DROPS), whose solution process utilizes the deterministic equivalent linear programming problem. We employ our STOCHGEN general purpose stochastic problem generator to ‘recreate’ the decision (scenario) tree for the unfolding future as this deterministic equivalent. To project random demands for oil products at different spatial locations into the future and to generate random fluctuations in their future prices/costs a stochastic input data simulator is developed and calibrated to historical industry data. The models are written in the modelling language XPRESS-MP and solved by the XPRESS suite of linear programming solvers. From the viewpoint of implementation of large-scale stochastic programming models this study involves decisions in both space and time and careful revision of the original deterministic formulation. The first part of the paper treats the specification, generation and solution of the deterministic DROP model. The stochastic version of the model (DROPS) and its implementation are studied in detail in the second part and a number of related research questions and implications discussed.     

Cure Cycle Design for Thermosetting-Matrix Composites Fabrication under Uncertainty

Design of the optimal cure temperature cycle is imperative for low-cost of manufacturing thermosetting-matrix composites. Uncertainties exist in several material and process parameters, which lead to variability in the process performance and product quality. This paper addresses the problem of determining the optimal cure temperature cycles under uncertainty. A stochastic model is developed, in which the parameter uncertainties are represented as probability density functions, and deterministic numerical process simulations based on the governing process physics are used to determine the distributions quantifying the output parameter variability. A combined Nelder–Mead Simplex method and the simulated annealing algorithm is used in conjunction with the stochastic model to obtain time-optimal cure cycles, subject to constraints on parameters influencing the product quality. Results are presented to illustrate the effects of a degree of parameter uncertainty, constraint values, and material kinetics on the optimal cycles. The studies are used to identify a critical degree of uncertainty in practice above which a rigorous analysis and design under uncertainty is warranted; below this critical value, a deterministic optimal cure cycle may be used with reasonable confidence.     

Effective averaging of stochastic radiative models based on Monte Carlo simulation

Based on the Monte Carlo simulation and probabilistic analysis, stochastic radiative models are effectively averaged; that is, deterministic models that reproduce the mean probabilities of particle passage through a stochastic medium are constructed. For this purpose, special algorithms for the double randomization and conjugate walk methods are developed. For the numerical simulation of stochastic media, homogeneous isotropic Voronoi and Poisson mosaic models are used. The parameters of the averaged models are estimated based on the properties of the exponential distribution and the renewal theory.     

The influence of forestry resources on rainfall: A deterministic and stochastic model

In this paper, we propose and analyze a deterministic model along with its stochastic version to address the problem of scanty rainfall by means of forestry resources. For deterministic model, boundedness of the system, feasibility of equilibria and their stability behavior are discussed. For stochastic model, boundedness, existence, uniqueness of global positive solution and sufficient conditions for the existence of unique stationary distribution are obtained. Model analysis reveals that the stability of the forest cover equilibrium state depends only on the model parameters in the deterministic case, however it also depends on the magnitude of the intensities of white noise terms in the stochastic case. To validate analytically obtained results and see the effect of key parameters, we have simulated proposed models using Indian annual rainfall data. The proposed model suggests that for the parameter values given in Table 2, the plantation of trees with slight higher intrinsic growth rate is beneficial to increase the rainfall.     

A recursive method to calculate the expected molecule numbers for a polymerization network with a small number of subunits

We compare the deterministic method and the stochastic method for a polymerization network when the number of available subunits is small. For the stochastic method, we prove there is a recursive method to compute the expected molecule numbers of various components in the reaction network, using the stationary probability distribution of molecule numbers which we illustrate to have a multivariate Poisson form. For the deterministic method, ordinary differential equations for the component concentrations are built following the mass action law. The steady state of the system is extracted to estimate the corresponding molecule numbers. Identities involving the propensity function parameters for the stochastic method and the reaction rate constants in the deterministic method are used to connect the two methods. Computations are conducted for a group of combinations of total number of subunits and reaction rate constant ratios, and the results are compared.     

Uncertainty quantification for linear elastic bodies with two fluctuating input parameters

In this work, we present a numerical method for solving partial differential equations (PDEs) with stochastic coefficients for a linear elastic body. To this end, a stochastic finite element method is applied. We distinguish two different cases for an isotropic material with two fluctuating input parameters in order to analyse the optimal choice of input parameters. Using the GALERKIN projection, the final stochastic equation system is reduced to a system of deterministic PDEs. Subsequently, the solution is determined iteratively. Finally, a numerical example for a plate with a ring hole is presented. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Investigation on effects of stochastic loading at the boundary under thermoelasticity with two relaxation parameters

The main objective of the present work is to study the responses of stochastic type mechanical distribution at the boundary of an elastic half space in the context of generalized thermoelasticity. In order to compare the results under the stochastic mechanical distribution, we have also considered the case of deterministic mechanical distribution prescribed at the boundary. The stochastic mechanical distribution is considered in such a way that it reduces to a deterministic type distribution as a special case. Laplace transform technique is used to solve the problem and its inversion is carried out by using asymptotic expansions valid for short times to obtain the solution of all the physical field variables like, stress and temperature distributions in the physical domain. Numerical results are found out to compare the effects of stochastic and deterministic load prescribed at the boundary of the elastic half space.     

Plastic Structural Analysis Under Stochastic Uncertainty

Problems from limit load or shakedown analysis are based on the convex, linear or linearized yield/strength condition and the linear equilibrium equation for the generic stress vector. Having to take into account, in practice, stochastic variations of the model parameters (e.g., yield stresses, plastic capacities) and external loadings, the basic stochastic plastic analysis problem must be replaced by an appropriate deterministic substitute problem. Instead of calculating approximatively the probability of failure based on a certain choice of failure modes, here, a direct approach is presented based on the costs for missing carrying capacity and the failure costs (e.g., costs for damage, repair, compensation for weakness within the structure, etc.). Based on the basic mechanical survival conditions, the failure costs may be represented by the minimum value of a convex and often linear program. Several mathematical properties of this program are shown. Minimizing then the total expected costs subject to the remaining (simple) deterministic constraints, a stochastic optimization problem is obtained which may be represented by a “Stochastic Convex Program (SCP) with recourse”. Working with linearized yield/strength conditions, a “Stochastic Linear Program (SLP) with complete fixed recourse” is obtained. In case of a discretely distributed probability distribution or after the discretization of a more general probability distribution of the random structural parameters and loadings as well as certain random cost factors one has a linear program (LP) with a so-called “dual decomposition data” structure. For stochastic programs of this type many theoretical results and efficient numerical solution procedures (LP-solver) are available. The mathematical properties of theses substitute problems are considered. Furthermore approximate analytical formulas for the limit load factor are given.     

Stochastic models for first-order kinetics of biochemical oxygen demand with random initial conditions, inputs, and coefficients

Biochemical oxygen demand (BOD) is a parameter of prime importance in surface water pollution studies and in the design and operation of waste-water treatment plants. A general, stochastic analytical model (denoted S1) is developed for the temporal expectation and (heteroscedastic) variance of first-order BOD kinetics. The model is obtained by integrating the moment equation, which is derived from the mathematical theory of stochastic differential equations. This model takes into account random initial conditions, random inputs, and random coefficients, which appear in the model formulation as initial condition (σ_O²), input (σ_l²), and coefficient (σ_c²) variance parameters, respectively. By constraining these three variance parameters to either vanish or to be nonnegative, model S1 is allowed (under appropriate combinations of the constraints) to split into six stochastic “submodels” (denoted S2 to S7), with each of these submodels being a particular case of the general model. Model S1 also degenerates to the deterministic model (denoted D) when each of the variance parameters vanish. The deterministic parameters (i.e., the rate coefficient and the ultimate BOD) and the stochastic variance parameters of the seven models are estimated on sets of replicated BOD data using the maximum likelihood principle. In this study, two (S5 and S7) of these seven stochastic models are found to be appropriate for BOD. The stochastic input (S5) model (i.e., null initial condition and coefficient variance parameters) shows the best prediction capabilities, while the next best is the stochastic initial condition (S7) model (i.e., null input and coefficient variance parameters).     

Optimization of Poincaré sections for discriminating between stochastic and deterministic behavior of dynamical systems

This paper studies the problem of finding optimal parameters for a Poincaré section used for determining the type of behavior of a time series: a deterministic or stochastic one. To reach that goal optimization algorithms are coupled with the Poincaré & Higuchi (P&H) method, which calculates the Higuchi dimension using points obtained by performing a Poincaré section of a certain attractor. The P&H method generates distinctive patterns that can be used for determining if a given attractor is produced by a deterministic or a stochastic system, but this method is sensitive to the parameters of the Poincaré section. Patterns generated by the P&H method can be characterized using numerical measures which in turn can be used for finding such parameters for the Poincaré section for which the patterns produced by the P&H method are the most prominent. This paper studies several approaches to parameterization of the Poincaré section. Proposed approaches are tested on twelve time series, six produced by deterministic chaotic systems and six generated randomly. The obtained results show, that finding good parameters of the Poincaré section is important for determining the type of behavior of a time series. Among the tested methods the evolutionary algorithm was able to find the best Poincaré sections for use with the P&H method.     

The Stochastic Quasi-chemical Model for Bacterial Growth: Variational Bayesian Parameter Update

We develop Bayesian methodologies for constructing and estimating a stochastic quasi-chemical model (QCM) for bacterial growth. The deterministic QCM, described as a nonlinear system of ODEs, is treated as a dynamical system with random parameters, and a variational approach is used to approximate their probability distributions and explore the propagation of uncertainty through the model. The approach consists of approximating the parameters’ posterior distribution by a probability measure chosen from a parametric family, through minimization of their Kullback–Leibler divergence.     

A dynamic stochastic version of the pitcher‐hamblin‐miller model of “collective violence"*

The central equation of the deterministic diffusion model of Pitcher, Hamblin, and Miller (1978) is formulated as a time‐inhomogeneous stochastic process. It will be shown that the stochastic process leads to a negative binomial distribution. The deterministic diffusion function can be derived from the stochastic model and is identical to the expected value as a function of time. Therefore the deterministic model is supported in terms of the underlying stochastic process. Moreover the stochastic model allows the prediction of the distribution for any point in time and the construction of prediction intervals.     

Stochastic MOLP with Incomplete Information: An Interactive Approach with Recourse

Numerous multiobjective linear programming (MOLP) methods have been proposed in the last two decades, but almost all for contexts where the parameters of problems are deterministic. However, in many real situations, parameters of a stochastic nature arise. In this paper, we suppose that the decision-maker is confronted with a situation of partial uncertainty where he possesses incomplete information about the stochastic parameters of the problem, this information allowing him to specify only the limits of variation of these parameters and eventually their central values. For such situations, we propose a multiobjective stochastic linear programming methodology; it implies the transformation of the stochastic objective functions and constraints in order to obtain an equivalent deterministic MOLP problem and the solving of this last problem by an interactive approach derived from the STEM method. Our methodology is illustrated by a didactical example.     

Average Case Analysis in Database Problems

In a variety of applications ranging from environmental and health sciences to bioinformatics, it is essential that data collected in large databases are generated stochastically. This states qualitatively new problems both for statistics and for computer science. Namely, instead of deterministic (usually worst case) analysis, the average case analysis is needed for many standard database problems. Since both stochastic and deterministic methods and notation are used it causes additional difficulties for an investigation of such problems and for an exposition of results. We consider a general class of probabilistic models for databases and study a few problems in a probabilistic framework. In order to demonstrate the general approach, the problems for systems of database constraints (keys, functional dependencies and related) are investigated in more detail. Our approach is based on consequent using Rényi entropy as a main characteristic of uncertainty of distribution and Poisson approximation (Stein–Chen technique) of the corresponding probabilities.     

Investigation of a problem of an elastic half space subjected to stochastic temperature distribution at the boundary

The present work investigates the responses of stochastic type temperature distribution applied at the boundary of an elastic medium in the context of thermoelasticity without energy dissipation. We consider an one dimensional problem of half space and assume that the bounding surface of the half space is traction free and is subjected to two types of time dependent temperature distributions which are of stochastic types. In order to compare the results predicted by stochastic temperature distributions with the results of deterministic type temperature distribution, the stochastic type temperature distributions applied at the boundary are taken in such a way that they reduce to the cases of deterministic types as special cases. Integral transform technique along with stochastic calculus is used to solve the problem. The approximated solutions for physical fields like, stress, temperature, displacement etc. are derived for very small values of time where stochastic type boundary conditions are taken to be of white noise type. The problem is further illustrated with graphical representation of numerical solutions of the problem for a particular case. A detailed comparison of the results of stochastic temperature, displacement and stress distributions inside the half space with the corresponding results of deterministic distributions is presented and special features of the effects of stochastic type boundary conditions are highlighted.     

Managing inventory and service levels in a safety stock‐based inventory routing system with stochastic retailer demands

The inherent uncertainty in supply chain systems compels managers to be more perceptive to the stochastic nature of the systems' major parameters, such as suppliers' reliability, retailers' demands, and facility production capacities. To deal with the uncertainty inherent to the parameters of the stochastic supply chain optimization problems and to determine optimal or close to optimal policies, many approximate deterministic equivalent models are proposed. In this paper, we consider the stochastic periodic inventory routing problem modeled as chance‐constrained optimization problem. We then propose a safety stock‐based deterministic optimization model to determine near‐optimal solutions to this chance‐constrained optimization problem. We investigate the issue of adequately setting safety stocks at the supplier's warehouse and at the retailers so that the promised service levels to the retailers are guaranteed, while distribution costs as well as inventory throughout the system are optimized. The proposed deterministic models strive to optimize the safety stock levels in line with the planned service levels at the retailers. Different safety stock models are investigated and analyzed, and the results are illustrated on two comprehensively worked out cases. We conclude this analysis with some insights on how safety stocks are to be determined, allocated, and coordinated in stochastic periodic inventory routing problem. Copyright © 2017 John Wiley & Sons, Ltd.