Title: Democratizing Deep Learning Development with DeepHyper Abstract: In recent years, deep neural networks (DNNs) have achieved considerable success in learning complex nonlinear relationships between features and targets from large datasets. Nevertheless, designing high-performing DNN architecture for a given data set is an expert-driven, time-consuming, trial-and-error manual task. A major bottleneck in the construction of […]
Title: Nonlinear Optimization at the National Labs Abstract: We review a range of challenging DOE applications in design and control that are formulated as nonlinear optimization problems, ranging from the modeling of gas and electricity networks, over the design of new materials, to the analysis of complex data. We show how research into new algorithms […]
Title: Physics-constrained data-driven methods for accurately accelerating simulations Abstract: A data-driven model can be built to accurately accelerate computationally expensive physical simulations, which is essential in multi-query problems, such as inverse problem, uncertainty quantification, design optimization, and optimal control. In this talk, two types of data-driven model order reduction techniques will be discussed, i.e., the […]