Statistical Analysis and Data Mining has just published a new special issue with selected papers from the Conference on Data Analysis. The special issue (14:6), guest edited by Max D. Morris, combines twelve papers describing research from talks and posters presented at CoDA 2020 and is now available to read here.
The Conference on Data Analysis (CoDA) is a biennial research meeting highlighting data-driven problems of interest to the United States Department of Energy. Talks and posters feature research from the Department of Energy national laboratories, academia, and industry. The conference has been hosted by the Statistical Sciences Group at Los Alamos National Laboratory in even-numbered years since 2012. CoDA 2020 was held February 25 – 27, 2020, in Santa Fe, NM. The following papers are included in the special issue:
- Learning compact physics-aware delayed photocurrent models using dynamic mode decomposition
- Comparison of machine learning approaches used to identify the drivers of Bakken oil well productivity
- Understanding the merits of winning data competition solutions for varied sets of objectives
- Identifying build orientation of 3D-printed materials using convolutional neural networks
- An approach to characterizing spatial aspects of image system blur
- An initial exploration of Bayesian model calibration for estimating the composition of rocks and soils on Mars
- A comparison of Gaussian processes and neural networks for computer model emulation and calibration
- Evaluating causal-based feature selection for fuel property prediction models
- A practical extension of the recursive multi-fidelity model for the emulation of hole closure experiments
- Fourier neural networks as function approximators and differential equation solvers
(Open Access) - Power grid frequency prediction using spatiotemporal modeling
- Precision aggregated local models