With the ever-increasing demand for more complex functionalities and miniaturization of photonic devices, the design of such devices requires a whole new different approach, deviating from classical photonic design approaches. Conventionally, the design of a typical photonic device starts with a prior knowledge, intuition, and practical experience of specific behavior...
Recently, machine learning and deep learning, which have made many theoretical and empir- ical breakthroughs and is widely applied in various fields, attract a great number of researchers and practitioners. They have become one of the most popular research directions and plays a sig- nificant role in many fields, such...
Optimization via simulation (OvS) is the practice of minimizing or maximizing the expected value of the output of a stochastic simulation model with respect to controllable decision variables. Stochastic simulation is a standard tool within operations research and is often required to model complex systems subject to uncertainty where it...
While optimization has received much attention in the machine learning community, most of them consider unconstrained supervised learning models such as neural networks and support vector machine. In this dissertation, we introduce a new class of optimization problems called scale invariant problems that include interesting unsupervised learning models such as...
Recovering three-dimensional (3D) structural information of a specimen from a single two-dimensional (2D) measurement remains an important but challenging task in microscopic imaging. A conventional 2D microscopic image has a shallow depth-of-focus (DoF). Thus, recovering 3D information usually requires sequentially z-scanning the focal planes. This process is time consuming and...
As the global population grows, consumption of water, energy, and food will also increase, placing stresses on these sectors, raising the importance of the Water-Energy-Food Nexus (WEFN). However, operation of WEFN systems are currently not sustainable. It is thus crucial to design WEFN systems to be sustainable from local to...
Stringent demands in the sheet forming industry related to rapid and customized part realization coupled with rigorous requirements on geometric accuracy and product properties have outpaced the capabilities of traditional forming processes. As an emerging novel technology, Double-Sided Incremental Forming (DSIF) offers much higher flexibility with the complete elimination of...
Traditionally, robust optimization has solved problems based on static decisions which are predetermined by the
decision makers. Once the decisions were made, the problem was solved and whenever a new uncertainty was
realized, the uncertainty was incorporated to the original problem and the entire problem was solved again to
account...
The chance-constrained method is one of the major approaches to solving optimization problems under various
uncertainties. It is a formulation of an optimization problem that ensures that the probability of meeting a certain
constraint is above a certain level. In other words, it restricts the feasible region so that the...
In this work, we will focus on the “at the same time” or direct transcription approach which allow a simultaneous
method for the dynamic optimization problem. In particular, we formulate the dynamic optimization model with
orthogonal collocation methods. These methods can also be regarded as a special class of implicit...