Uncertainty Management in the Design of Multiscale Systems

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Ayan Sinha ◽  
Nilanjan Bera ◽  
Janet K. Allen ◽  
Jitesh H. Panchal ◽  
Farrokh Mistree
Keyword(s):  
Robust Design ◽  
Design Method ◽  
Computational Cost ◽  
Cost Benefit ◽  
Simulation Models ◽  
System Level ◽  
Model Refinement ◽  
Trade Off ◽  
Multiscale Systems ◽  
Model Parameter Uncertainty

In this paper, the opportunities for managing uncertainty in simulation-based design of multiscale systems are explored using constructs from information management and robust design. A comprehensive multiscale design problem, the concurrent design of material and product is used to demonstrate our approach. The desired accuracy of the simulated performance is determined by the trade-off between computational cost for model refinement and the benefits of mitigated uncertainty from the refined models. Our approach consists of integrating: (i) a robust design method for multiscale systems and (ii) an improvement potential based approach for quantifying the cost-benefit trade-off for reducing uncertainty in simulation models. Specifically, our approach focuses on allocating resources for reducing model parameter uncertainty arising due to insufficient data from simulation models. Using this approach, system level designers can efficiently allocate resources for sequential simulation model refinement in multiscale systems.

Managing Uncertainty in Multiscale Systems via Simulation Model Refinement

2011 ◽  
Author(s):  
Ayan Sinha ◽  
Jitesh H. Panchal ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Simulation Model ◽  
Design Method ◽  
Information Economics ◽  
Computational Cost ◽  
Cost Benefit ◽  
Simulation Models ◽  
System Level ◽  
Model Refinement ◽  
Trade Off ◽  
Multiscale Systems

The motivating question for this article is: ‘How should a system level designer allocate resources for auxiliary simulation model refinement while satisfying system level design objectives and ensuring robust process requirements in multiscale systems? Our approach consists of integrating: (i) a robust design method for multiscale systems (ii) an information economics based approach for quantifying the cost-benefit trade-off for mitigating uncertainty in simulation models. Specifically, the focus is on allocating resources for reducing model parameter uncertainty arising due to insufficient data from simulation models. A comprehensive multiscale design problem, the concurrent design of material and product is used for validation. The multiscale system is simulated with models at multiple length and time scales. The accuracy of the simulated performance is determined by the trade-off between computational cost for model refinement and the benefits of mitigated uncertainty from the refined models. System level designers can efficiently allocate resources for sequential simulation model refinement in multiscale systems using this approach.

scholarly journals The computational cost of active information sampling prior to decision making under uncertainty

2020 ◽  
Author(s):  
Pierre Petitet ◽  
Bahaaeddin Attaallah ◽  
Sanjay G Manohar ◽  
Masud Husain
Keyword(s):  
Time Pressure ◽  
Computational Cost ◽  
Computational Modelling ◽  
Cost Benefit ◽  
Theoretical Models ◽  
Cognitive Effort ◽  
Related Factor ◽  
Trade Off ◽  
Hidden Cost ◽  
Information Sampling

Humans often seek information to minimise the pervasive effect of uncertainty on decisions. Current theories explain how much knowledge people should gather prior to a decision, based on the cost-benefit structure of the problem at hand. Here, we demonstrate that this framework omits a crucial agent-related factor: the cognitive effort expended while collecting information. Using a novel paradigm, we unveil a speed-efficiency trade-off whereby more informative samples actually take longer to find. Crucially, under sufficient time pressure, humans can break this trade-off, sampling both faster and more efficiently. Computational modelling demonstrates the existence of a hidden cost of cognitive effort which, when incorporated into theoretical models, provides a better account of people's behaviour and also predicts self-reported fatigue accumulated during active sampling. By measuring metacognitive accuracy and uncertainty-reward preferences on a static, passive version of the task, we further validate the theoretical constructs captured by our model. Overall, the results show that the way people seek knowledge to guide their decisions is shaped not only by task-related costs and benefits, but also crucially by the quantifiable computational costs incurred.

scholarly journals Robust Product Design: A Modern View of Quality Engineering in Manufacturing Systems

2019 ◽  
Author(s):  
Amir Parnianifard ◽  
SITI AZFANIZAM AHMAD ◽  
M.K.A. Ariffin ◽  
M.I.S. Ismai
Keyword(s):  
Robust Design ◽  
Manufacturing Systems ◽  
Design Method ◽  
Computational Cost ◽  
Optimization Methods ◽  
Manufacturing Processes ◽  
Quality Loss Function ◽  
High Quality ◽  
Different Types ◽  
Robust Design Method

One of the main technological and economic challenges for an engineer is designing high-quality products in manufacturing processes. Most of these processes involve a large number of variables included the setting of controllable (design) and uncontrollable (noise) variables. Robust Design (RD) method uses a collection of mathematical and statistical tools to study a large number of variables in the process with a minimum value of computational cost. Robust design method tries to make high-quality products according to customers’ viewpoints with an acceptable profit margin. This paper aims to provide a brief up-to-date review of the latest development of RD method particularly applied in manufacturing systems. The basic concepts of the quality loss function, orthogonal array, and crossed array design are explained. According to robust design approach, two classifications are presented, first for different types of factors, and second for different types of data. This classification plays an important role in determining the number of necessity replications for experiments and choose the best method for analyzing data. In addition, the combination of RD method with some other optimization methods applied in designing and optimizing of processes are discussed.

scholarly journals Robust Product Design: A Modern View of Quality Engineering in Manufacturing Systems

2018 ◽  
Author(s):  
Amir Parnianifard ◽  
A.S. Azfanizama ◽  
M.K.A. Ariffin ◽  
M.I.S. Ismai
Keyword(s):  
Robust Design ◽  
Manufacturing Systems ◽  
Design Method ◽  
Computational Cost ◽  
Optimization Methods ◽  
Manufacturing Processes ◽  
Quality Loss Function ◽  
High Quality ◽  
Different Types ◽  
Robust Design Method

One of the main technological and economic challenges for an engineer is designing high-quality products in manufacturing processes. Most of these processes involve a large number of variables included the setting of controllable (design) and uncontrollable (noise) variables. Robust Design (RD) method uses a collection of mathematical and statistical tools to study a large number of variables in the process with a minimum value of computational cost. Robust design method tries to make high-quality products according to customers’ viewpoints with an acceptable profit margin. This paper aims to provide a brief up-to-date review of the latest development of RD method particularly applied in manufacturing systems. The basic concepts of the quality loss function, orthogonal array, and crossed array design are explained. According to robust design approach, two classifications are presented, first for different types of factors, and second for different types of data. This classification plays an important role in determining the number of necessity replications for experiments and choose the best method for analyzing data. In addition, the combination of RD method with some other optimization methods applied in designing and optimizing of processes are discussed.

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

2020 ◽  
Vol 2020 (14) ◽  
pp. 378-1-378-7
Author(s):  
Tyler Nuanes ◽  
Matt Elsey ◽  
Radek Grzeszczuk ◽  
John Paul Shen
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Computational Cost ◽  
False Positives ◽  
Compact Model ◽  
High Quality ◽  
False Negatives ◽  
Trade Off ◽  
Depth Reconstruction ◽  
Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

Towards Automated Evaluation of Vehicle Dynamics in System-Level Designs

2012 ◽  
Author(s):  
Zsolt Lattmann ◽  
Adam Nagel ◽  
Jason Scott ◽  
Kevin Smyth ◽  
Chris vanBuskirk ◽  
...  
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Test Bench ◽  
Simulation Models ◽  
Mobility Model ◽  
System Level ◽  
Bond Graphs ◽  
Automated Evaluation ◽  
Design Alternatives ◽  
Automatic Composition

We describe the use of the Cyber-Physical Modeling Language (CyPhyML) to support trade studies and integration activities in system-level vehicle designs. CyPhyML captures parameterized component behavior using acausal models (i.e. hybrid bond graphs and Modelica) to enable automatic composition and synthesis of simulation models for significant vehicle subsystems. Generated simulations allow us to compare performance between different design alternatives. System behavior and evaluation are specified independently from specifications for design-space alternatives. Test bench models in CyPhyML are given in terms of generic assemblies over the entire design space, so performance can be evaluated for any selected design instance once automated design space exploration is complete. Generated Simulink models are also integrated into a mobility model for interactive 3-D simulation.

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