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pSeven is a DSE (Design Space Exploration) software platform developed by DATADVANCE, extending design, simulation and analysis capabilities and assisting in faster design decisions. It provides integration with third party CAD and CAE software tools, multi-objective and robust optimization algorithms, data analysis, and uncertainty quantification tools. pSeven comes under the notion of PIDO (Process Integration and Design Optimization) software. Design Space Exploration functionality is based on the mathematical algorithms of pSeven Core (formerly known as MACROS) Python library, also developed by DATADVANCE. SmartSelection technology implemented in pSeven automatically selects the most efficient method for a given data or optimization problem that makes advance math easy to use to a wide range of experts. History The foundation for the pSeven Core library as pSeven's background was laid in 2003, when the researchers from the Institute for Information Transmission Problems ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
PSeven Logo
pSeven is a DSE (Design Space Exploration) software platform developed by DATADVANCE, extending design, simulation and analysis capabilities and assisting in faster design decisions. It provides integration with third party CAD and CAE software tools, multi-objective and robust optimization algorithms, data analysis, and uncertainty quantification tools. pSeven comes under the notion of PIDO (Process Integration and Design Optimization) software. Design Space Exploration functionality is based on the mathematical algorithms of pSeven Core (formerly known as MACROS) Python library, also developed by DATADVANCE. SmartSelection technology implemented in pSeven automatically selects the most efficient method for a given data or optimization problem that makes advance math easy to use to a wide range of experts. History The foundation for the pSeven Core library as pSeven's background was laid in 2003, when the researchers from the Institute for Information Transmission Problems ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Latin Hypercube Sampling
Latin hypercube sampling (LHS) is a statistical method for generating a near-random sample of parameter values from a multidimensional distribution. The sampling method is often used to construct computer experiments or for Monte Carlo integration. LHS was described by Michael McKay of Los Alamos National Laboratory in 1979. An independently equivalent technique was proposed by Vilnis Eglājs in 1977. It was further elaborated by Ronald L. Iman and coauthors in 1981. Detailed computer codes and manuals were later published. In the context of statistical sampling, a square grid containing sample positions is a Latin square if (and only if) there is only one sample in each row and each column. A Latin hypercube is the generalisation of this concept to an arbitrary number of dimensions, whereby each sample is the only one in each axis-aligned hyperplane containing it. When sampling a function of N variables, the range of each variable is divided into M equally probable interv ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Engineering Optimization
Engineering optimization is the subject which uses optimization techniques to achieve design goals in engineering. It is sometimes referred to as design optimization. Topics * structural design (including pressure vessel design and welded beam design) * shape optimization * topology optimization (including airfoils) * inverse optimization (a subset of the inverse problem) * processing planning * product designs * electromagnetic optimization * space mapping * aggressive space mapping The space mapping methodology for modeling and design optimization of engineering systems was first discovered by John Bandler in 1993. It uses relevant existing knowledge to speed up model generation and design optimization of a system. The kno ...J.E. Rayas-Sanche"Power in simplicity with ASM: tracing the aggressive space mapping algorithm over two decades of development and engineering applications" IEEE Microwave Magazine, vol. 17, no. 4, pp. 64-76, April 2016. * yield-driven design * opt ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Predictive Analytics
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detecti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dimension Reduction
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable (hard to control or deal with). Dimensionality reduction is common in fields that deal with large numbers of observations and/or large numbers of variables, such as signal processing, speech recognition, neuroinformatics, and bioinformatics. Methods are commonly divided into linear and nonlinear approaches. Approaches can also be divided into feature selection and feature extraction. Dimensionality reduction can be used for noise reduction, data visualization, cluster analysis, or as an intermediate ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Uncertainty Quantification
Uncertainty quantification (UQ) is the science of quantitative characterization and reduction of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known. An example would be to predict the acceleration of a human body in a head-on crash with another car: even if the speed was exactly known, small differences in the manufacturing of individual cars, how tightly every bolt has been tightened, etc., will lead to different results that can only be predicted in a statistical sense. Many problems in the natural sciences and engineering are also rife with sources of uncertainty. Computer experiments on computer simulations are the most common approach to study problems in uncertainty quantification. Sources Uncertainty can enter mathematical models and experimental measurements in various contexts. One way to categorize the sources of uncertainty is to consider: ; Paramet ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dependence Analysis
In compiler theory, dependence analysis produces execution-order constraints between statements/instructions. Broadly speaking, a statement ''S2'' depends on ''S1'' if ''S1'' must be executed before ''S2''. Broadly, there are two classes of dependencies--control dependencies and data dependencies. Dependence analysis determines whether it is safe to reorder or parallelize statements. Control dependencies Control dependency is a situation in which a program instruction executes if the previous instruction evaluates in a way that allows its execution. A statement ''S2'' is ''control dependent'' on ''S1'' (written S1\ \delta^c\ S2) if and only if ''S2s execution is conditionally guarded by ''S1''. ''S2'' is ''control dependent'' on ''S1'' if and only if S1 \in PDF(S2) where PDF(S) is the post dominance frontier of statement S. The following is an example of such a control dependence: S1 if x > 2 goto L1 S2 y := 3 S3 L1: z := y + 1 Here, ''S2'' only runs if t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sensitivity Analysis
Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty; ideally, uncertainty and sensitivity analysis should be run in tandem. The process of recalculating outcomes under alternative assumptions to determine the impact of a variable under sensitivity analysis can be useful for a range of purposes, including: * Testing the robustness of the results of a model or system in the presence of uncertainty. * Increased understanding of the relationships between input and output variables in a system or model. * Uncertainty reduction, through the identification of model input that cause significant uncertainty in the output and should therefore be the focus of attention in order to increase robustness (perhap ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Design Of Experiments
The design of experiments (DOE, DOX, or experimental design) is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associated with experiments in which the design introduces conditions that directly affect the variation, but may also refer to the design of quasi-experiments, in which natural conditions that influence the variation are selected for observation. In its simplest form, an experiment aims at predicting the outcome by introducing a change of the preconditions, which is represented by one or more independent variables, also referred to as "input variables" or "predictor variables." The change in one or more independent variables is generally hypothesized to result in a change in one or more dependent variables, also referred to as "output variables" or "response variables." The experimental design may also identify control variables that must be h ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Response Surface Methodology
In statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. The method was introduced by George E. P. Box and K. B. Wilson in 1951. The main idea of RSM is to use a sequence of designed experiments to obtain an optimal response. Box and Wilson suggest using a second-degree polynomial model to do this. They acknowledge that this model is only an approximation, but they use it because such a model is easy to estimate and apply, even when little is known about the process. Statistical approaches such as RSM can be employed to maximize the production of a special substance by optimization of operational factors. Of late, for formulation optimization, the RSM, using proper design of experiments (DoE), has become extensively used. In contrast to conventional methods, the interaction among process variables can be determined by statistical techniques. Basic approach of response surfa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Optimal Design
In the design of experiments, optimal designs (or optimum designs) are a class of experimental designs that are optimal with respect to some statistical criterion. The creation of this field of statistics has been credited to Danish statistician Kirstine Smith. In the design of experiments for estimating statistical models, optimal designs allow parameters to be estimated without bias and with minimum variance. A non-optimal design requires a greater number of experimental runs to estimate the parameters with the same precision as an optimal design. In practical terms, optimal experiments can reduce the costs of experimentation. The optimality of a design depends on the statistical model and is assessed with respect to a statistical criterion, which is related to the variance-matrix of the estimator. Specifying an appropriate model and specifying a suitable criterion function both require understanding of statistical theory and practical knowledge with designing exper ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
