2023/11/29 by Kellin Rumsey, Rumsey, Kellin N., Zachary K. Hardy +5 · 4 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Methodology (stat.ME) #Model Reduction and Neural Networks #Neural Networks and Reservoir Computing #Protein Structure and Dynamics
paper · pdf · doi:10.48550/arxiv.2311.18146
openalex publication_date 2023/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Active subspace (AS) methods are a valuable tool for understanding the relationship between the inputs and outputs of a Physics simulation. In this paper, an elegant generalization of the traditional ASM is developed to assess the co-activity of two computer models. This generalization, which we refer to as a Co-Active Subspace (C-AS) Method, allows for the joint analysis of two or more computer models allowing for thorough exploration of the alignment (or non-alignment) of the respective gradient spaces. We define co-active directions, co-sensitivity indices, and a scalar ``concordance" metric (and complementary ``discordance" pseudo-metric) and we demonstrate that these are powerful tools for understanding the behavior of a class of computer models, especially when used to supplement traditional AS analysis. Details for efficient estimation of the C-AS and an accompanying R package (github.com/knrumsey/concordance) are provided. Practical application is demonstrated through analyzing a set of simulated rate stick experiments for PBX 9501, a high explosive, offering insights into complex model dynamics.