Visual Steering Commands for Trade Space Exploration: User-Guided Sampling With Example

2007 ◽  
Author(s):  
Gary Stump ◽  
Sara Lego ◽  
Mike Yukish ◽  
Timothy W. Simpson ◽  
Joseph A. Donndelinger
Keyword(s):  
Design Space ◽  
Space Exploration ◽  
Pareto Frontier ◽  
Configuration Model ◽  
Future Research ◽  
Scatter Matrix ◽  
Computing Power ◽  
Point Of Interest ◽  
High Preference ◽  
Trade Space Exploration

Recent advancements in computing power and speed provide opportunities to revolutionize trade space exploration, particularly for the design of complex systems such as automobiles, aircraft, and spacecraft. In this paper, we introduce three Visual Steering Commands to support trade space exploration and demonstrate their use within a powerful data visualization tool that allows designers to explore multidimensional trade spaces using glyph, 1-D and 2-D histogram, 2-D scatter, scatter matrix, and parallel coordinate plots; linked views; brushing; preference shading and Pareto frontier display. In particular, we define three user-guided samplers that enable designers to explore (1) the entire design space, (2) near a point of interest, or (3) within a region of high preference. We illustrate these three samplers with a vehicle configuration model that evaluates the technical feasibility of new vehicle concepts. Future research is also discussed.

Visual Steering Commands for Trade Space Exploration: User-Guided Sampling With Example

2009 ◽  
Vol 9 (4) ◽  
Author(s):  
Gary Stump ◽  
Sara Lego ◽  
Mike Yukish ◽  
Timothy W. Simpson ◽  
Joseph A. Donndelinger
Keyword(s):  
Design Space ◽  
Space Exploration ◽  
Pareto Frontier ◽  
Configuration Model ◽  
Future Research ◽  
Scatter Matrix ◽  
Computing Power ◽  
Point Of Interest ◽  
High Preference ◽  
Trade Space Exploration

Recent advancements in computing power and speed provide opportunities to revolutionize trade space exploration, particularly for the design of complex systems such as automobiles, aircraft, and spacecraft. In this paper, we introduce three visual steering commands to support trade space exploration and demonstrate their use within a powerful data visualization tool that allows designers to explore multidimensional trade spaces using glyph, 1D and 2D histograms, 2D scatter, scatter matrix, and parallel coordinate plots, linked views, brushing, preference shading, and Pareto frontier display. In particular, we define three user-guided samplers that enable designers to explore (1) the entire design space, (2) near a point of interest, or (3) within a region of high preference. We illustrate these three samplers with a vehicle configuration model that evaluates the technical feasibility of new vehicle concepts. Future research is also discussed.

The Importance of Training for Interactive Trade Space Exploration: A Study of Novice and Expert Users

2011 ◽  
Vol 11 (3) ◽  
Author(s):  
David Wolf ◽  
Jennifer Hyland ◽  
Timothy W. Simpson ◽  
Xiaolong (Luke) Zhang
Keyword(s):  
Space Exploration ◽  
Design Decision ◽  
Support Design ◽  
Computing Power ◽  
Training Video ◽  
Engine Combustion ◽  
Interactive Data ◽  
Effectiveness And Efficiency ◽  
Trade Space Exploration ◽  
Future Work

Thanks to recent advances in computing power and speed, engineers can now generate a wealth of data on demand to support design decision-making. These advances have enabled new approaches to search multidimensional trade spaces through interactive data visualization and exploration. In this paper, we investigate the effectiveness and efficiency of interactive trade space exploration strategies by conducting human subject experiments with novice and expert users. A single objective, constrained design optimization problem involving the sizing of an engine combustion chamber is used for this study. Effectiveness is measured by comparing the best feasible design obtained by each user, and efficiency is assessed based on the percentage of feasible designs generated by each user. Results indicate that novices who watch a 5-min training video before the experiment obtain results that are not significantly different from those obtained by expert users, and both groups are statistically better than the novices without the training video in terms of effectiveness and efficiency. Frequency and ordering of the visualization and exploration tools are also compared to understand the differences in each group’s search strategy. The implications of the results are discussed along with future work.

P3GA: An Algorithm for Technology Characterization

2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Edgar Galvan ◽  
Richard J. Malak
Keyword(s):  
Design Automation ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Numerical Test ◽  
Pareto Frontier ◽  
Test Problems ◽  
Genetic Optimization ◽  
Technology Evaluation ◽  
Mathematical Basis

It is important for engineers to understand the capabilities and limitations of the technologies they consider for use in their systems. However, communicating this information can be a challenge. Mathematical characterizations of technical capabilities are of interest as a means to reduce ambiguity in communication and to increase opportunities to utilize design automation methods. The parameterized Pareto frontier (PPF) was introduced in prior work as a mathematical basis for modeling technical capabilities. One advantage of PPFs is that, in many cases, engineers can model a system by composing frontiers of its components. This allows for rapid technology evaluation and design space exploration. However, finding the PPF can be difficult. The contribution of this article is a new algorithm for approximating the PPF, called predictive parameterized Pareto genetic algorithm (P3GA). The proposed algorithm uses concepts and methods from multi-objective genetic optimization and machine learning to generate a discrete approximation of the PPF. If needed, designers can generate a continuous approximation of the frontier by generalizing beyond these data. The algorithm is explained, its performance is analyzed on numerical test problems, and its use is demonstrated on an engineering example. The results of the investigation indicate that P3GA may be effective in practice.

Evaluating the Performance of Visual Steering Commands for User-Guided Pareto Frontier Sampling During Trade Space Exploration

2008 ◽  
Author(s):  
Dan Carlsen ◽  
Matthew Malone ◽  
Josh Kollat ◽  
Timothy W. Simpson
Keyword(s):  
Decision Making ◽  
Design Optimization ◽  
Optimization Problems ◽  
Space Exploration ◽  
Pareto Frontier ◽  
Optimization Process ◽  
Multi Objective Genetic Algorithm ◽  
Trade Space Exploration ◽  
Support Engineering ◽  
Visualization Techniques

Trade space exploration is a promising decision-making paradigm that provides a visual and more intuitive means for formulating, adjusting, and ultimately solving design optimization problems. This is achieved by combining multi-dimensional data visualization techniques with visual steering commands to allow designers to “steer” the optimization process while searching for the best, or Pareto optimal, designs. In this paper, we compare the performance of different combinations of visual steering commands implemented by two users to a multi-objective genetic algorithm that is executed “blindly” on the same problem with no human intervention. The results indicate that the visual steering commands — regardless of the combination in which they are invoked — provide a 4x–7x increase in the number of Pareto solutions that are obtained when the human is “in-the-loop” during the optimization process. As such, this study provides the first empirical evidence of the benefits of interactive visualization-based strategies to support engineering design optimization and decision-making. Future work is also discussed.

scholarly journals Enhanced function-means modeling supporting design space exploration

2019 ◽  
Vol 33 (4) ◽  
pp. 502-516 ◽  
Author(s):  
Jakob R. Müller ◽  
Ola Isaksson ◽  
Jonas Landahl ◽  
Visakha Raja ◽  
Massimo Panarotto ◽  
...  
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Future Research ◽  
The Novel ◽  
Alternative Design ◽  
Model Flexibility ◽  
Function Models ◽  
Alternative Designs ◽  
Alternative Solutions

AbstractOne problem in incremental product development is that geometric models are limited in their ability to explore radical alternative design variants. In this publication, a function modeling approach is suggested to increase the amount and variety of explored alternatives, since function models (FM) provide greater model flexibility. An enhanced function-means (EF-M) model capable of representing the constraints of the design space as well as alternative designs is created through a reverse engineering process. This model is then used as a basis for the development of a new product variant. This work describes the EF-M model's capabilities for representing the design space and integrating novel solutions into the existing product structure and explains how these capabilities support the exploration of alternative design variants. First-order analyses are executed, and the EF-M model is used to capture and represent already existing design information for further analyses. Based on these findings, a design space exploration approach is developed. It positions the FM as a connection between legacy and novel designs and, through this, allows for the exploration of more diverse product concepts. This approach is based on three steps – decomposition, design, and embodiment – and builds on the capabilities of EF-M to model alternative solutions for different requirements. While the embodiment step of creating the novel product's geometry is still a topic for future research, the design space exploration concept can be used to enable wider, more methodological, and potentially automated design space exploration.

Use Scenarios for Design Space Exploration With a Dynamic Multiobjective Optimization Formulation

2012 ◽  
Author(s):  
Shane K. Curtis ◽  
Braden J. Hancock ◽  
Christopher A. Mattson
Keyword(s):  
Multiobjective Optimization ◽  
Conceptual Design ◽  
Design Space Exploration ◽  
Optimization Problem ◽  
Design Space ◽  
Optimization Problems ◽  
Space Exploration ◽  
Pareto Frontier ◽  
Problem Formulation ◽  
Design And Optimization

In a recent publication, we presented a new strategy for engineering design and optimization, which we termed formulation space exploration. The formulation space for an optimization problem is the union of all variable and design objective spaces identified by the designer as being valid and pragmatic problem formulations. By extending a computational search into this new space, the solution to any optimization problem is no longer predefined by the optimization problem formulation. This method allows a designer to both diverge the design space during conceptual design and converge onto a solution as more information about the design objectives and constraints becomes available. Additionally, we introduced a new way to formulate multiobjective optimization problems, allowing the designer to change and update design objectives, constraints, and variables in a simple, fluid manner that promotes exploration. In this paper, we investigate three use scenarios where formulation space exploration can be utilized in the early stages of design when it is possible to make the greatest contributions to development projects. Specifically, we look at s-Pareto frontier generation in the formulation space, formulation space boundary exploration, and a new way to perform inverse optimization. The benefits of these methods are illustrated with the conceptual design of an impact driver.

Design Space Exploration of a 512KB STT-Assisted SOT MRAM Cache

2020 ◽  
Author(s):  
Adrian G. Caburnay ◽  
Jonathan Gabriel S.A. Reyes ◽  
Anastacia P. Ballesil-Alvarez ◽  
Maria Theresa G. de Leon ◽  
John Richard E. Hizon ◽  
...  
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration
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