# Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies

arXiv: https://arxiv.org/abs/1912.08113 · HTML: https://mlsanity.com/paper/1912.08113/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 14 of 14)

- [Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era](https://mlsanity.com/paper/2310.15816/) (2023)
- [Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers](https://mlsanity.com/paper/2110.06968/) (2021)
- [Predicting Wind-Driven Spatial Deposition through Simulated Color Images using Deep Autoencoders](https://mlsanity.com/paper/2202.01762/) (2022)
- [Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data](https://mlsanity.com/paper/2312.03642/) (2023)
- [PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion](https://mlsanity.com/paper/2603.04606/) (2026)
- [Suppressing simulation bias using multi-modal data](https://mlsanity.com/paper/2104.09684/) (2021)
- [Mixed Diagnostics for Longitudinal Properties of Electron Bunches in a Free-Electron Laser](https://mlsanity.com/paper/2201.05769/) (2022)
- [Causal Multi-fidelity Surrogate Forward and Inverse Models for ICF Implosions](https://mlsanity.com/paper/2509.05510/) (2025)
- [Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion](https://mlsanity.com/paper/2111.12798/) (2021)
- [Thinner Latent Spaces: Detecting Dimension and Imposing Invariance with Conformal Autoencoders](https://mlsanity.com/paper/2408.16138/) (2024)
- [DiffUNet^2: Bidirectional Conditional Diffusion for Probabilistic Scientific Spatiotemporal Modeling](https://mlsanity.com/paper/2606.03926/) (2026)
- [Co4ICF: Co-evolving Physics-Informed Surrogate and RL-based Pulse Optimizer for Inertial Confinement Fusion](https://mlsanity.com/paper/2607.10366/) (2026)
- [Cycle-Consistent and Uncertainty-Aware Neural Surrogates for Tokamak Edge Plasmas](https://mlsanity.com/paper/2607.21407/) (2026)
- [Adaptive Conformal Prediction for Image Regression Models with Application to an Inertial Confinement Fusion Emulator](https://mlsanity.com/paper/2610.00535/) (2026)

## Abstract (arXiv)

Neural networks have become very popular 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 consistent with the physical manifold -- i.e., predictions are always physically meaningful, and are cyclically consistent -- i.e., when the predictions of the surrogate, when passed through an independently trained inverse model give back the original input parameters. We find that these two consistencies lead to surrogates that are superior in terms of predictive performance, 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 1D semi-analytic numerical simulator and demonstrate the effectiveness of our approach. Code and data are available at https://github.com/rushilanirudh/macc/