Optimal Experimental Design Methods Research Articles

Augmenting a Waste Glass Mixture Experiment Study with Additional Glass Components and Experimental Runs

A glass composition variation study (CVS) for high-level waste (HLW) stored in Idaho is being statistically designed and performed in phases over several years. The purpose of the CVS is to investigate and model how HLW-glass properties depend on glass composition. The resulting glass property–composition models will be used to develop desirable glass formulations and for other purposes. Phases 1 and 2 of the CVS have been completed and are briefly described. This paper focuses on the CVS Phase 3 experimental design, which was chosen to augment the Phase 1 and 2 data with additional data points, as well as to account for additional glass components not studied in Phases 1 and/or 2. In total, 16 glass components were varied in the Phase 3 experimental design. The paper describes how these Phase 3 experimental design augmentation challenges were addressed using the previous data, preliminary property–composition models, and statistical mixture experiment and optimal experimental design methods and software. The resulting Phase 3 experimental design of 30 simulated HLW glasses is presented and discussed.

Systems Analysis in Ground‐Water Planning and Management

The objective of this paper is to review the state of the art of systems analysis and optimization techniques developed in the field of water resources for the planning and management of a ground‐water system. The areas reviewed include the following: ground‐water management models, inverse solution techniques for parameter identification, and optimal experimental design methods. Emphasis is placed upon ground‐water supply management models, as opposed to models used for ground‐water quality management. The techniques that have been used in the optimization of ground‐water management include: linear programming, mixed‐integer and quadratic programming, differential dynamic programming, nonlinear programming, and simulation. The inverse problem of parameter identification pertains the optimal determination of model parameters using historical input and output observations. Because of data limitation in both quantity and quality, the inverse problem is inherently ill posed. This paper summarizes recent advances made in the inverse procedures and methods developed to alleviate the problems of instability ard nonuniqueness of the identified parameters. The optimal experimental design problem addresses the issue of data requirements and optimal sampling strategies for the purpose of parameter identification. A criterion must be established for the optimal design of a pumping test. The fundamental concept of optimal experimental design and various criteria used for optimization are reviewed.

A Method for Data Reduction and Optimal Experimental Design in XPD

A Method for Data Reduction and Optimal Experimental Design in XPD

Numerical methods of optimal unblased experimental design in a Hilbert space

Numerical methods of optimal unblased experimental design in a Hilbert space

The estimation of mutation rates when premeiotic events are involved.

When mutation or recombination events occur premeiotically, the distribution of exceptional individuals among the offspring will be "clustered" as opposed to binomial. Even though the exact nature of the clustering is usually unknown, unbiased methods for measuring mutation rate and determining the precision of these measurements are given to replace a biased method now frequently used. When clustering is pronounced, the unweighted average mutation rate is found to be a more efficient estimator than the usual average weighted by family size. Methods of statistical inference and optimal experimental design in the absence of specific knowledge of the mechanism of clustering are also discussed.