The task of autonomous and robust design cannot be regarded as a single task, but consists of two tasks that have to be accomplished concurrently. First, the design should be found autonomously; this indicates the existence of a method which is able to find the optimal design choice automatically. Second, the design should be robust; in other words: the design should be safeguarded against unce...

This paper introduces four new methods for robust design optimization (RDO) of complex engineering systems. The methods involve polynomial dimensional decomposition (PDD) of a high-dimensional stochastic response for statistical moment analysis, a novel integration of PDD and score functions for calculating the secondmoment sensitivities with respect to the design variables, and standard gradie...

Enzyme catalysts of a retroaldol reaction have been generated by computational design using a motif that combines a lysine in a nonpolar environment with water-mediated stabilization of the carbinolamine hydroxyl and β-hydroxyl groups. Here, we show that the design process is robust and repeatable, with 33 new active designs constructed on 13 different protein scaffold backbones. The initial ac...

The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality. Working within the design space is not considered as a change. Movement out of the design space is considered to be a change and would normally initiate a regulatory post approval change process. Design space is propo...

Profile-guided code transformations specialize program code according to the profile provided by execution on training data. Consequently, the performance of the code generated usind this feedback is sensitive to the selection of training data. Used in this fashion, the principle behind profileguided optimization techniques is the same as off-line learning commonly used in the field of machine ...

This paper extends and applies the strategies recently developed by the authors for handling constraints under uncertainty to robust design optimization. For the scope of this paper, robust optimization is a methodology aimed at problems for which some parameters are uncertain and are only known to belong to some uncertainty set. This set can be described by either a deterministic or a probabil...

This work deals with the multiobjective robust design optimization of rail vehicle systems moving in short radius curved tracks. Two criteria are considered simultaneously, i.e., safety and comfort. We showed that the deterministic optimal solutions, for the nominal design parameters, can be altered seriously by the design parameters uncertainty. A new algorithm that combines Genetic Algorithms...

Abstract: This paper presents a new procedure for discrete-time robust structured control design. Parameter-dependent nonconvex conditions for stabilizable and induced L2-norm performance controllers are solved by an iterative linear matrix inequalities (LMI) optimization. A wide class of controller structures including decentralized of any order, fixed-order dynamic output feedback, static out...

This study is devoted to discrete sizing optimization of truss structures employing an efficient discrete evolutionary meta-heuristic algorithm which uses the Newton gradient-based method as its updating scheme and it is named here as Newton Meta-heuristic Algorithm (NMA). In order to enable the NMA population-based meta-heuristic to effectively explore the discrete design space, a term contain...