EVOLVE International Conference

EVOLVE 2014 July 1-4
Beijing, China

HomeTracks and sessionsRobust Optimization

Robust Optimization

Organizer: Massimiliano Vasile


Uncertainty quantification is a growing field of research, however its integration with computational optimization is still an emerging technology with great potential to address some of the challenges that our world is facing. The fusion of uncertainty quantification and computational optimization, generally referred to as robust optimization or optimization under uncertainty, is expected to have a significant impact on: engineering design and manufacturing (optimal design of product and processes under uncertainty), climate change and control (optimal design of human activities under uncertainty), finance, system biology, drug protocol definition, energy production and distribution, sustainable building design and control, just to mention a few key areas. In an expanding world with limited resources, optimization becomes essential. However, our representation of the world is often incomplete and affected by errors. The inclusion of uncertainties in the optimization process is therefore of primary importance to design solutions that are weakly affected by uncertainties and failure averse.

This session aims at promoting a constructive discussion on open problems, required advances and possible applications of computational optimization and uncertainty quantification with the precise goal to identify new avenues of collaborative research and development.

You are kindly invited to contribute to the session with a talk, and a paper, on the
following subjects:
- Design for reliability
- Optimization Under Uncertainty
- Imprecise Probabilities
- Subjective Probabilities and Elicitation
- Applications of Uncertainty Quantification and Robust Design to Real Engineering Problems
- Uncertainty Quantification and Robust Optimization in Manufacturing Processes
- Climate Uncertainty Quantification and Control
- Robust Design of Drugs and Drug Protocols
- Uncertainty Quantification in Model-Based Design
- Uncertainty Quantification and Data Analysis for Inverse Problems
- Model Reduction
- Uncertainty Quantification and Optimization of Dynamical Systems
- Uncertainty Quantification in Multi-fidelity Approaches

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