Improved surrogates in inertial confinement fusion with manifold and cycle consistencies

Significance Neural networks have demonstrated remarkable success in predictive modeling. However, when applied to surrogate modeling, they 1) are often nonrobust, 2) require large amounts of data, and 3) are inadequate for estimating the inversion process; i.e., they do not capture parameter sensitivities well. We propose a different form of self-consistency regularization by incorporating an inverse surrogate into the learning process and show that it leads to highly robust, self-consistent surrogate models for complex scientific applications. Neural networks have become the method of choice in surrogate modeling because of their ability to characterize arbitrary, high-dimensional functions in a data-driven fashion. This paper advocates for the training of surrogates that are 1) consistent with the physical manifold, resulting in physically meaningful predictions, and 2) cyclically consistent with a jointly trained inverse model; i.e., backmapping predictions through the inverse results in the original input parameters. We find that these two consistencies lead to surrogates that are superior in terms of predictive performance, are more resilient to sampling artifacts, and tend to be more data efficient. Using inertial confinement fusion (ICF) as a test-bed problem, we model a one-dimensional semianalytic numerical simulator and demonstrate the effectiveness of our approach.

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