Zobrazeno 1 - 10
of 141
pro vyhledávání: '"Ling, Julia"'
Autor:
Hegde, Vinay I., Borg, Christopher K. H., del Rosario, Zachary, Kim, Yoolhee, Hutchinson, Maxwell, Antono, Erin, Ling, Julia, Saxe, Paul, Saal, James E., Meredig, Bryce
A central challenge in high throughput density functional theory (HT-DFT) calculations is selecting a combination of input parameters and post-processing techniques that can be used across all materials classes, while also managing accuracy-cost trad
Externí odkaz:
http://arxiv.org/abs/2007.01988
A cylindrical and inclined jet in crossflow is studied under two distinct velocity ratios, $r=1$ and $r=2$, using highly resolved large eddy simulations (LES). First, an investigation of turbulent scalar mixing sheds light onto the previously observe
Externí odkaz:
http://arxiv.org/abs/2001.04600
Materials discovery is often compared to the challenge of finding a needle in a haystack. While much work has focused on accurately predicting the properties of candidate materials with machine learning (ML), which amounts to evaluating whether a giv
Externí odkaz:
http://arxiv.org/abs/1911.11201
Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning. However, standard \emph{global-scope error} metrics for model quality are not predictive of discovery performance,
Externí odkaz:
http://arxiv.org/abs/1911.03224
The design of film cooling systems relies heavily on Reynolds-Averaged Navier-Stokes (RANS) simulations, which solve for mean quantities and model all turbulent scales. Most turbulent heat flux models, which are based on isotropic diffusion with a fi
Externí odkaz:
http://arxiv.org/abs/1910.03097
Autor:
Aditya, Konduri, Kolla, Hemanth, Kegelmeyer, W. Philip, Shead, Timothy M., Ling, Julia, Davis IV, Warren L.
We propose an anomaly detection method for multi-variate scientific data based on analysis of high-order joint moments. Using kurtosis as a reliable measure of outliers, we suggest that principal kurtosis vectors, by analogy to principal component an
Externí odkaz:
http://arxiv.org/abs/1808.04498
Autor:
Hutchinson, Maxwell L., Antono, Erin, Gibbons, Brenna M., Paradiso, Sean, Ling, Julia, Meredig, Bryce
Despite increasing focus on data publication and discovery in materials science and related fields, the global view of materials data is highly sparse. This sparsity encourages training models on the union of multiple datasets, but simple unions can
Externí odkaz:
http://arxiv.org/abs/1711.05099
Autor:
Ling, Julia, Hutchinson, Maxwell, Antono, Erin, DeCost, Brian, Holm, Elizabeth A., Meredig, Bryce
As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure. Given the importance of process-structure-property relations throughout materia
Externí odkaz:
http://arxiv.org/abs/1711.00404
Publikováno v:
Integrating Materials and Manufacturing Innovation, (2017)
The optimization of composition and processing to obtain materials that exhibit desirable characteristics has historically relied on a combination of scientist intuition, trial and error, and luck. We propose a methodology that can accelerate this pr
Externí odkaz:
http://arxiv.org/abs/1704.07423
Although an increased availability of computational resources has enabled high-fidelity simulations of turbulent flows, the RANS models are still the dominant tools for industrial applications. However, the predictive capabilities of RANS models are
Externí odkaz:
http://arxiv.org/abs/1701.07102