Refine Now, Query Fast: A Decoupled Refinement Paradigm for Implicit Neural Fields

• URL: http://arxiv.org/abs/2602.15155v2
• Date: Sun, 22 Feb 2026 05:07:51 GMT
• Title: Refine Now, Query Fast: A Decoupled Refinement Paradigm for Implicit Neural Fields
• Authors: Tianyu Xiong, Skylar Wurster, Han-Wei Shen,
• Abstract summary: Implicit Neural Representations (INRs) have emerged as promising surrogates for large 3D scientific simulations.<n>We propose a paradigm for building powerful and practical neural field surrogates and revINRs in broader applications.
• Score: 31.861702750709256
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Implicit Neural Representations (INRs) have emerged as promising surrogates for large 3D scientific simulations due to their ability to continuously model spatial and conditional fields, yet they face a critical fidelity-speed dilemma: deep MLPs suffer from high inference cost, while efficient embedding-based models lack sufficient expressiveness. To resolve this, we propose the Decoupled Representation Refinement (DRR) architectural paradigm. DRR leverages a deep refiner network, alongside non-parametric transformations, in a one-time offline process to encode rich representations into a compact and efficient embedding structure. This approach decouples slow neural networks with high representational capacity from the fast inference path. We introduce DRR-Net, a simple network that validates this paradigm, and a novel data augmentation strategy, Variational Pairs (VP) for improving INRs under complex tasks like high-dimensional surrogate modeling. Experiments on several ensemble simulation datasets demonstrate that our approach achieves state-of-the-art fidelity, while being up to 27$\times$ faster at inference than high-fidelity baselines and remaining competitive with the fastest models. The DRR paradigm offers an effective strategy for building powerful and practical neural field surrogates and \rev{INRs in broader applications}, with a minimal compromise between speed and quality.

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