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Neural networks have become very popular in surrogate modeling because oftheir ability to characterize arbitrary, high dimensional functions in a datadriven fashion. This paper advocates for the training of surrogates that areconsistent with the physical manifold -- i.e., predictions are alwaysphysically meaningful, and are cyclically consistent -- i.e., when thepredictions of the surrogate, when passed through an independently trainedinverse model give back the original input parameters. We find that these twoconsistencies lead to surrogates that are superior in terms of predictiveperformance, more resilient to sampling artifacts, and tend to be more dataefficient. Using Inertial Confinement Fusion (ICF) as a test bed problem, wemodel a 1D semi-analytic numerical simulator and demonstrate the effectivenessof our approach. Code and data are available athttps://github.com/rushilanirudh/macc/

Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies