Advances in optimization technology, a cornerstone in engineering modeling, simulation-based design and manufacturing, continue to push back the boundaries of feasibility. Multi-disciplinary optimization continues to show success. Notwithstanding advances in computing power and user-friendly management of multidisciplinary software, challenging problems will undoubtedly continue to plague the d...

Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1,...

Surrogate modeling uses cheap “surrogates” to represent the response surface of simulation models. It involves several steps, including initial sampling, regression and adaptive sampling. This study evaluates an adaptive surrogate modeling based optimization (ASMO) method on two benchmark problems: the Hartman function and calibration of the SAC-SMA hydrologic model. Our results show that: 1) G...

Abstract Surrogate models play a vital role in overcoming the computational challenge designing and analyzing nonlinear dynamic systems, especially presence of uncertainty. This paper presents comparative study different surrogate modeling techniques for systems. Four methods, namely, Gaussian process (GP) regression, long short-term memory (LSTM) network, convolutional neural network (CNN) wit...

Surrogate models, also referenced as metamodels, have emerged attractive data-driven, predictive models for storm surge estimation. They are calibrated based on an existing database of synthetic simulations and can provide fast-to-compute approximations the expected surge, replacing numerical model that was used to establish this database. This paper discusses specifically development a kriging...