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 […]
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 […]
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 […]