44 citations · 47 across the 2 of their papers we have counts for
2 papers
cs.LG2021★ 44 cited
Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks
Franz M. Rohrhofer, Stefan Posch, Clemens Gößnitzer +1
Physics-informed neural networks (PINNs) have emerged as a promising deep learning method, capable of solving forward and inverse problems governed by differential equations. Despi…
stat.AP2014★ 3 cited
Supervised Penalized Canonical Correlation Analysis
Andrea Thum, Susann Mönchgesang, Lore Westphal +4
The canonical correlation analysis (CCA) is commonly used to analyze data sets with paired data, e.g. measurements of gene expression and metabolomic intensities of the same experi…