Quantifying the Impact of Lossy Compression on Neural Generative Surrogate Modeling
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...
| Authors | Zhimin Li, Harshitha Menon, Charles Jekel et al. |
|---|---|
| Primary category | cs.DC |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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