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.

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.

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.

Simulation Model Refinement for Decision Making Via a Value-of-Information Based Metric

2006 ◽  
Author(s):  
Jitesh H. Panchal ◽  
Christiaan J. J. Paredis ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Decision Making ◽  
Simulation Model ◽  
Value Of Information ◽  
Design Space ◽  
Simulation Models ◽  
Model Refinement ◽  
Physical Behavior ◽  
Additional Information ◽  
A Value ◽  
The Difference

Since no simulation model is perfect, any simulation model for modeling a system’s physical behavior can be refined further. Hence, the question faced by a designer is — “How much refinement of a simulation model is adequate for a particular design problem?” To answer this question, we present a value-of-information based approach for determining the appropriate extent of refinement of simulation models. The value of additional information obtained via refinement of simulation models is measured as the difference between the maximum payoff that could possibly be achieved throughout the design space and the minimum possible payoff at the point in the design space selected using the simple model. The approach is presented using two examples — design of a pressure vessel and the design of a material.

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.

scholarly journals System Level Simulation of Microgrid Power Electronic Systems

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 644
Author(s):  
Michal Frivaldsky ◽  
Jan Morgos ◽  
Michal Prazenica ◽  
Kristian Takacs
Keyword(s):  
Smart Grid ◽  
Simulation Model ◽  
Real Time ◽  
Power Converter ◽  
System Level ◽  
Time Behavior ◽  
Operational Parameters ◽  
System Level Simulation ◽  
Time Operation ◽  
The Impact

In this paper, we describe a procedure for designing an accurate simulation model using a price-wised linear approach referred to as the power semiconductor converters of a DC microgrid concept. Initially, the selection of topologies of individual power stage blocs are identified. Due to the requirements for verifying the accuracy of the simulation model, physical samples of power converters are realized with a power ratio of 1:10. The focus was on optimization of operational parameters such as real-time behavior (variable waveforms within a time domain), efficiency, and the voltage/current ripples. The approach was compared to real-time operation and efficiency performance was evaluated showing the accuracy and suitability of the presented approach. The results show the potential for developing complex smart grid simulation models, with a high level of accuracy, and thus the possibility to investigate various operational scenarios and the impact of power converter characteristics on the performance of a smart gird. Two possible operational scenarios of the proposed smart grid concept are evaluated and demonstrate that an accurate hardware-in-the-loop (HIL) system can be designed.

scholarly journals Guidelines to Calibrate a Multi-Residential Building Simulation Model Addressing Overheating Evaluation and Residents’ Influence

Buildings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 242
Author(s):  
Christoph Schünemann ◽  
David Schiela ◽  
Regine Ortlepp
Keyword(s):  
Simulation Model ◽  
Time Window ◽  
Residential Building ◽  
Simulation Models ◽  
Building Simulation ◽  
Night Time ◽  
Building Performance Simulation ◽  
Starting Point ◽  
3D Simulation Model ◽  
Building Physics

Can building performance simulation reproduce measured summertime indoor conditions of a multi-residential building in good conformity? This question is answered by calibrating simulated to monitored room temperatures of several rooms of a multi-residential building for an entire summer in two process steps. First, we did a calibration for several days without the residents being present to validate the building physics of the 3D simulation model. Second, the simulations were calibrated for the entire summer period, including the residents’ impact on evolving room temperature and overheating. As a result, a high degree of conformity between simulation and measurement could be achieved for all monitored rooms. The credibility of our results was secured by a detailed sensitivity analysis under varying meteorological conditions, shading situations, and window ventilation or room use in the simulation model. For top floor dwellings, a high overheating intensity was evoked by a combination of insufficient use of night-time window ventilation and non-heat-adapted residential behavior in combination with high solar gains and low heat storage capacities. Finally, the overall findings were merged into a process guideline to describe how a step-by-step calibration of residential building simulation models can be done. This guideline is intended to be a starting point for future discussions about the validity of the simplified boundary conditions which are often used in present-day standard overheating assessment.

Settings, Experimentation and Evaluation of the Simulation Models

2013 ◽  
Vol 309 ◽  
pp. 366-371 ◽  
Author(s):  
František Manlig ◽  
Radek Havlik ◽  
Alena Gottwaldova
Keyword(s):  
Simulation Model ◽  
New Technology ◽  
Simulation Models ◽  
Manufacturing Processes ◽  
Customer Requirements ◽  
Evaluation Functions ◽  
Operational Management ◽  
Minimum Number ◽  
User Friendly ◽  
Setting Parameters

This paper deals with research in computer simulation of manufacturing processes. The paper summarizes the procedures associated with developing the model, experimenting with and evaluating the model results. The key area is of experimentation with the simulation model and evaluation using indicators or multi-criteria functions. With regards to the experiment the crucial variables are the simulation model. The key ideas are to set the number of variables, depending on what a given simulation will be. For example, when introducing new technology into production, modify the type of warehouse, saving workers, thus economizing. The simulation models for the operational management uses simplified models, if possible, a minimum number of variables to obtain the result in shortest possible time. These models are more user friendly and the course will be conducted mostly in the background. An example of a criteria function is the number of parts produced or production time. Multi-criteria function has given us the opportunity to make better quality decisions. It is based on the composition of several parameters, including their weight to one end point. The type of evaluation functions, whether it is an indicator or criteria function is selected and based on customer requirements. In most cases it is recommended to use the multi-dimensional function. It gives us a more comprehensive view of the results from the model and facilitates decision-making. The result of this paper is a display of setting parameters for the experimentation on a sample model. Furthermore, the comparisons of results with a multi-criteria objective function and one-criterion indicator.

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