Quantifying the Impact of Lossy Compression on Neural Generative Surrogate Modeling

2026-06-14

AI Summary

This study addresses the storage and I/O bottlenecks faced by high-fidelity neural surrogate models, which stem from their reliance on large-scale training data, and tackles the challenge of quantifying the impact of lossy compression errors on model performance. The work proposes a novel method that leverages the inherent stochasticity of neural network training to assess error tolerance through uncertainty quantification, enabling a controllable trade-off between compression ratio and training efficiency while preserving model accuracy. Evaluated on two scientific simulation tasks, the approach achieves up to 39× data compression with up to 3× reduction in training time, while incurring negligible degradation in model quality.

Abstract

Neural networks are used as generative surrogate models for scientific discovery, which are trainable approximations of scientific simulations. These models enable users to replace time-consuming numerical simulations with learned alternatives, providing quick solutions. However, high-fidelity generative surrogate models require massive training datasets, which can create storage and I/O challenges. Lossy compression is a promising way to reduce this burden, but compression errors may affect the model quality in subtle ways, making it challenging to quantify their impact. In this work, we examine how lossy compression of training data impacts the quality of generative surrogate models. We begin by characterizing the uncertainty inherent in training neural networks, showing that identical training configurations can produce different models. By exploiting this variability, we propose a method to estimate how much compression-induced error a surrogate model can tolerate without affecting its accuracy. Evaluation of two application simulations demonstrates that our approach significantly reduces memory/storage requirements and speeds up training while producing high-quality surrogate models. These results show that lossy compression saves data storage up to 23.7x and 39x with negligible impact on the quality of the surrogate model. Meanwhile, reducing the size of the training data set also enhances the data loading speed and reduces the training time by up to 3x.

Problem

• lossy compression
• generative surrogate modeling
• neural networks
• model quality
• compression error

Innovation

• lossy compression
• generative surrogate modeling
• neural network uncertainty
• compression tolerance
• scientific simulation acceleration