A5000786245- Advanced Multi-Objective Optimization Algorithms
- Probabilistic and Robust Engineering Design
- Manufacturing Process and Optimization
- Optimal Experimental Design Methods
- Water resources management and optimization
- Advanced Control Systems Optimization
- Constraint Satisfaction and Optimization
- Computational Fluid Dynamics and Aerodynamics
- Matrix Theory and Algorithms
- Groundwater flow and contamination studies
- Advanced Optimization Algorithms Research
- Simulation Techniques and Applications
- Iterative Learning Control Systems
- Gear and Bearing Dynamics Analysis
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Mechanical Engineering and Vibrations Research
- Sports Dynamics and Biomechanics
- Water Systems and Optimization
- VLSI and FPGA Design Techniques
- Statistics Education and Methodologies
- Sports Analytics and Performance
- Numerical methods in inverse problems
Boeing (Australia)
2006-2012
Boeing (United States)
2002-2008
Seattle University
2002
North Carolina State University
2001
This paper presents a mathematical model for basketball free throws. It is intended to be supplement an existing calculus course and could easily used as basis project. Students will learn how apply interesting real-world problem, from problem identification all the way through interpretation verification. Along we introduce topics such optimization (univariate multiobjective), numerical methods, differential equations.
Computer simulations are an integral part of many today’s design processes. This paper describes a method for exploration and optimization that is flrstly mathematically suitable functions calculated with complex computer secondly made practical parallel computing. These assertions conflrmed numerical results. As shown several examples, using the also has side beneflts in data consistency, use engineering time, explaining results context. I. Introduction They can be used to explore space...
An emerging need in industry is to do simulation based designs with several hundred design variables. Our current approach, as implemented Design Explorer, not practical for problems of this size. This paper explains these limitations and presents a new approach that allows us overcome them. Some the issues associated using codes have been attacked, while others remain open. addresses arise when problem has large number We call our MoVars \more variables or Multidisciplinary Optimization Via...
In many engineering applications the need arises to solve multi-objective optimization problems that involve computationally expensive functions. A well accepted approach for is Normal-Boundary Intersection (NBI) by Das and Dennis. We can run NBI either on surrogate models of simulations or directly simulations. However, neither these alternatives eective complex functions; surrogates are not accurate enough, using too costly. have found a unique solves this dilemma. 2 Our combines use in...
Solving multi-objective optimization problems that involve computationally expensive functions is a normal part of many engineering applications. Runtime issues are magnied when one moves from single discipline to multiple disciplines in multidisciplinary design setting. The tools selected perform space exploration within this setting must be chosen with care. Normal-Boundary Intersection (NBI) robust general purpose method for problems. In the functions, running NBI directly using...
Over the last few years great progress has been made in design space exploration methods. These methods have common that they generally address problems involving long running computer codes. They can try to provide an understanding of relationship between inputs and outputs, perform single- or multi-objective optimizations, under uncertainty. To successfully apply these appropriate architectures are needed. need take into account runtime methods, fact codes fail for certain, even valid,...