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Bayesian Surrogate Learning for Uncertainty Analysis of Coupled Multidisciplinary Systems
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Engineering systems are often composed of many subsystems that interact with each other. These subsystems, referred to as disciplines, contain many types of uncertainty and in many cases are feedback-coupled with each ...
Adaptive Dimensionality Reduction for Fast Sequential Optimization With Gaussian Processes
Publisher: American Society of Mechanical Engineers (ASME)
Abstract: Available computational models for many engineering design applications are both expensive and and of a black-box nature. This renders traditional optimization techniques difficult to apply, including gradient-based ...
How Diverse Initial Samples Help and Hurt Bayesian Optimizers
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Design researchers have struggled to produce quantitative predictions for exactly why and when diversity might help or hinder design search efforts. This paper addresses that problem by studying one ubiquitously used search ...
Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual-Phase Materials
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Integrated Computational Materials Engineering (ICME) calls for the integration of computational tools into the materials and parts development cycle, while the Materials Genome Initiative (MGI) calls for the acceleration ...