scholarly journals Modelling stochastic behaviour in simulation digital twins through neural nets

2021 ◽  
pp. 1-14
Author(s):  
Sean Reed ◽  
Magnus Löfstrand ◽  
John Andrews
Keyword(s):  
Neural Nets ◽  
Stochastic Behaviour ◽  
Digital Twins

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

Synthesis of digital twins with use of multidimensional recursive decomposition

2019 ◽  
pp. 26-37
Author(s):  
V. A. Minaev ◽  
A. V. Mazin ◽  
K. B. Zdiruk ◽  
L. S. Kulikov
Keyword(s):  
Educational Institution ◽  
Subject Area ◽  
Information Storage ◽  
Information Space ◽  
Practical Implementation ◽  
Cognitive Approach ◽  
Recursive Decomposition ◽  
Digital Twins ◽  
Secondary Information ◽  
Integrated Information

The article presents the scientific and methodological issues of formation of digital twins collections based on the use of the multi-aspect recursive decomposition algorithm of the subject area. The general approaches to the solution of topical issues of the modern stage of artificial intelligence are considered. The terminology is concretized in the interrelated areas of knowledge – information – data and its relation with the term of «digital twins» as information containers of knowledge is discussed. The structure, power estimation and metrizability of the information space presented as a recursively defined ordered set of elements – a collection of digital twins (DT-collections) are considered. It is shown that the practical implementation of this approach and its application as part of automated control systems involves maintaining the life cycle of the creation and operation of digital twins in the Integrated information storage, implementing a two-circuit scheme (model) of management. A new cognitive approach to assess the completeness of the knowledge measure in the information space is proposed. The model of the integrated information storage realizing accumulation of knowledge in data banks of primary and secondary information is considered. As an example, a recursive decomposition of a subset of engineering systems of an educational institution is performed.

How to Assess Prognostic Models for Survival Data: A Case Study in Oncology

2003 ◽  
Vol 42 (05) ◽  
pp. 564-571 ◽  
Author(s):  
M. Schumacher ◽  
E. Graf ◽  
T. Gerds
Keyword(s):  
Test Data ◽  
Survival Data ◽  
Prediction Error ◽  
Classification Scheme ◽  
Neural Nets ◽  
Brier Score ◽  
Data Set ◽  
Independent Test ◽  
Artificial Neural

Summary Objectives: A lack of generally applicable tools for the assessment of predictions for survival data has to be recognized. Prediction error curves based on the Brier score that have been suggested as a sensible approach are illustrated by means of a case study. Methods: The concept of predictions made in terms of conditional survival probabilities given the patient’s covariates is introduced. Such predictions are derived from various statistical models for survival data including artificial neural networks. The idea of how the prediction error of a prognostic classification scheme can be followed over time is illustrated with the data of two studies on the prognosis of node positive breast cancer patients, one of them serving as an independent test data set. Results and Conclusions: The Brier score as a function of time is shown to be a valuable tool for assessing the predictive performance of prognostic classification schemes for survival data incorporating censored observations. Comparison with the prediction based on the pooled Kaplan Meier estimator yields a benchmark value for any classification scheme incorporating patient’s covariate measurements. The problem of an overoptimistic assessment of prediction error caused by data-driven modelling as it is, for example, done with artificial neural nets can be circumvented by an assessment in an independent test data set.

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