Multi-level, partitioned response surfaces for modeling complex systems

1998 ◽  
Author(s):  
Patrick Koch ◽  
Dimitri Mavris ◽  
Farrokh Mistree
Keyword(s):  
Complex Systems ◽  
Response Surfaces ◽  
Multi Level

Catalogue Design of Technical Systems Based on a Resource Exchange Paradigm

1996 ◽  
Author(s):  
Michael Heinrich ◽  
Werner E. Juengst
Keyword(s):  
Complex Systems ◽  
Mechanical Systems ◽  
Resource Exchange ◽  
Short Introduction ◽  
Multi Level ◽  
Technical Systems

Abstract In this paper, we illustrate the use of the resource exchange paradigm for mechanical systems and, through multi-level configuration, for complex systems. To make this paper self-contained, a short introduction to resource-based modelling is included.

Complex systems models for causal inference in social epidemiology

2020 ◽  
Vol 75 (7) ◽  
pp. 702-708
Author(s):  
Hiba N Kouser ◽  
Ruby Barnard-Mayers ◽  
Eleanor Murray
Keyword(s):  
Complex Systems ◽  
Causal Inference ◽  
Social Inequalities ◽  
Social Epidemiology ◽  
Causal Effects ◽  
Natural Choice ◽  
Systems Models ◽  
Multi Level

Systems models, which by design aim to capture multi-level complexity, are a natural choice of tool for bridging the divide between social epidemiology and causal inference. In this commentary, we discuss the potential uses of complex systems models for improving our understanding of quantitative causal effects in social epidemiology. To put systems models in context, we will describe how this approach could be used to optimise the distribution of COVID-19 response resources to minimise social inequalities during and after the pandemic.

scholarly journals Multi-fidelity modelling via recursive co-kriging and Gaussian–Markov random fields

2015 ◽  
Vol 471 (2179) ◽  
pp. 20150018 ◽  
Author(s):  
P. Perdikaris ◽  
D. Venturi ◽  
J. O. Royset ◽  
G. E. Karniadakis
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Stochastic Modelling ◽  
Response Surfaces ◽  
Circular Cylinders ◽  
Computationally Efficient ◽  
Initial State ◽  
Gaussian Markov Random Fields ◽  
Markov Random ◽  
Multi Level

We propose a new framework for design under uncertainty based on stochastic computer simulations and multi-level recursive co-kriging. The proposed methodology simultaneously takes into account multi-fidelity in models, such as direct numerical simulations versus empirical formulae, as well as multi-fidelity in the probability space (e.g. sparse grids versus tensor product multi-element probabilistic collocation). We are able to construct response surfaces of complex dynamical systems by blending multiple information sources via auto-regressive stochastic modelling. A computationally efficient machine learning framework is developed based on multi-level recursive co-kriging with sparse precision matrices of Gaussian–Markov random fields. The effectiveness of the new algorithms is demonstrated in numerical examples involving a prototype problem in risk-averse design, regression of random functions, as well as uncertainty quantification in fluid mechanics involving the evolution of a Burgers equation from a random initial state, and random laminar wakes behind circular cylinders.

On Using Kriging Models for Complex Design

2011 ◽  
Author(s):  
Jay D. Martin
Keyword(s):  
Complex Systems ◽  
Robust Design ◽  
Kriging Model ◽  
Response Surfaces ◽  
Optimal System ◽  
System Level ◽  
Design Teams ◽  
Complex Design ◽  
Level Performance ◽  
Estimate System

The design of most modern systems requires the tight integration of multiple disciplines. In practice, these multiple disciplines are often optimized independently, given only fixed values or targets for their interactions with other disciplines. The result is a system that may not represent the optimal system-level design. It may also not be a robust design in the sense that small changes in each subsystem’s performance may have a large impact on the system-level performance. The use of kriging models to represent the response surfaces of subsystems that are then combined to estimate system-level performance can be used as a method to provide collaboration between design teams. The difficulty with this method is the creation of the models given potentially large number of dimensions or observations. This paper presents a method to reduce the dimensionality of the input space for kriging models used for designing of complex systems. The input dimensionality of the kriging model is reduced to only includes the most important factors needed for the prediction of the observed output. A result of using these reduced dimensionality models is the need to no longer force interpolation of all of the observations used to create the models.

Partitioned, multilevel response surfaces for modeling complex systems

AIAA Journal ◽  
2000 ◽  
Vol 38 ◽  
pp. 875-881
Author(s):  
Patrick N. Koch ◽  
Dimitri Mavris ◽  
Farrokh Mistree
Keyword(s):  
Complex Systems ◽  
Response Surfaces

scholarly journals From inert matter to the global society life as multi-level networks of processes

2020 ◽  
Vol 375 (1796) ◽  
pp. 20190329 ◽  
Author(s):  
David Chavalarias
Keyword(s):  
Complex Systems ◽  
Biological Networks ◽  
Theoretical Biology ◽  
Life Forms ◽  
New Paradigm ◽  
Philosophical Foundations ◽  
Process Networks ◽  
The Face ◽  
Multi Level ◽  
To Come

A few billion years have passed since the first life forms appeared. Since then, life has continued to forge complex associations between the different emergent levels of interconnection it forms. The advances of recent decades in molecular chemistry and theoretical biology, which have embraced complex systems approaches, now make it possible to conceptualize the questions of the origins of life and its increasing complexity from three complementary notions of closure: processes closure, autocatalytic closure and constraints closure. Developed in the wake of the second-order cybernetics, this triple closure approach, that relies on graph theory and complex networks science, sketch a paradigm where it is possible to go up the physical levels of organization of matter, from physics to biology and society, without resorting to strong reductionism. The phenomenon of life is conceived as the contingent complexification of the organization of matter, until the emergence of life forms, defined as a network of auto-catalytic process networks, organized in a multi-level manner. This approach of living systems, initiated by Maturana & Varela and Kauffman, inevitably leads to a reflection on the nature of cognition; and in the face of the deep changes that affected humanity as a complex systems, on the nature of cultural evolution. Faced with the major challenges that humanity will have to address in the decades to come, this new paradigm invites us to change our conception of causality by shifting our attention from state change to process change and to abandon a widespread notion of 'local' causality in favour of complex systems thinking. It also highlights the importance of a better understanding of the influence of social networks, recommendation systems and artificial intelligence on our future collective dynamics and social cognition processes. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.

Multi-level approach for important component identification and reliability evaluation for complex system by survival signature

2019 ◽  
Vol 37 (1) ◽  
pp. 262-288
Author(s):  
Liling Ge ◽  
Yingjie Zhang
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
Reliability Analysis ◽  
Production System ◽  
Content Type ◽  
Actual Production ◽  
Machining System ◽  
Multi Level ◽  
Critical Components

Purpose The purpose of this paper is to identify the critical components of a complex system by using survival signature. First, a complex system is abstracted with varying scales and generates a multi-levels model. Then reliability evaluations can be conducted by survival signature from rough to fine for tracing and identifying them. Finally, the feasibility of the proposed approach is demonstrated by an actual production system. Design/methodology/approach The paper mainly applies a multi-level evaluating strategy for the reliability analysis of complex systems with components of multiple types. In addition, a multi-levels model of a complex system is constructed and survival signature also used for evaluation. Findings The proposed approach was demonstrated to be the feasibility by an actual production system that is used in the case study. Research limitations/implications The case study was performed on a system with simple network structure, but the proposed approach could be applied to systems with complex ones. However, the approach to generate the digraphs of abstraction levels for complex system has to be developed. Practical implications So far the approach has been used for the reliability analysis of a machining system. The approach that is proposed for the identification of critical components also can be applied to make maintenance decision. Originality/value The multi-level evaluating strategy that was proposed for reliability analysis and the identification of critical components of complex systems was a novel method, and it also can be applied as index to make maintenance planning.

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