Placement-Aware Quantization for Fractional-PDE Neural Surrogates

Research poster presented at the WiSys SPARK Symposium. The work examines component-level quantization sensitivity in Fourier Neural Operators (FNOs) applied to fractional partial differential equation surrogates. Results show that middle-core operator blocks tolerate simulated 8-bit quantization while projection-side layers require higher precision, motivating a placement-aware mixed-precision strategy to reduce compute overhead while preserving numerical accuracy and discrete PDE consistency.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-21
DOI
https://doi.org/10.5281/zenodo.22044287
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
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article

Placement-Aware Quantization for Fractional-PDE Neural Surrogates

Chaewon Yoon
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
article

Placement-Aware Quantization for Fractional-PDE Neural Surrogates

Chaewon Yoon
article en

Abstract

Research poster presented at the WiSys SPARK Symposium. The work examines component-level quantization sensitivity in Fourier Neural Operators (FNOs) applied to fractional partial differential equation surrogates. Results show that middle-core operator blocks tolerate simulated 8-bit quantization while projection-side layers require higher precision, motivating a placement-aware mixed-precision strategy to reduce compute overhead while preserving numerical accuracy and discrete PDE consistency.

Zenodo (CERN European Organization for Nuclear Research)
University of Wisconsin–Oshkosh (US)
Openalex Percentile: Top 10%
Model Reduction and Neural Networks
DOIOpen Access PDFOpenAlex
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Placement-Aware Quantization for Fractional-PDE Neural Surrogates — Chaewon Yoon · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS