Scalable, probabilistic simulation in a distributed design environment

Integrated simulations have been used to predict and analyze the integrated behavior of large, complex product and technology systems throughout their design cycles. During the process of integration, uncertainties arise from many sources, such as material properties, manufacturing variations, inaccuracy of models and so on. Concerns about uncertainty and robustness in large-scale integrated design can be significant, especially under the situations where the system performance is sensitive to the variations. Probabilistic simulation can be an important tool to enable uncertainty analysis, sensitivity analysis, risk assessment and reliability-based design in integrated simulation environments. Monte Carlo methods have been widely used to resolve probabilistic simulation problems. To achieve desired estimation accuracy, typically a large number of samples are needed. However, large integrated simulation systems are often computationally heavy and time-consuming due to their complexity and large scale, making the conventional Monte Carlo approach computationally prohibitive. This work focuses on developing an efficient and scalable approach for probabilistic simulations in integrated simulation environments. A predictive machine learning and statistical approach is proposed in this thesis.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Mechanical Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• Mao, Wei, Ph. D. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• David R. Wallace.

• Mechanical Engineering.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/55254

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/55254