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 INRs in broader applications, with a minimal compromise between speed and quality.

翻译：隐式神经表示（INR）因能连续建模空间场和条件场，已成为大型三维科学仿真的有前途替代方案，但它们面临一个关键的真实度-速度困境：深层MLP推理成本高昂，而基于嵌入的高效模型缺乏足够表达能力。为解决这一问题，我们提出了解耦表示精炼（DRR）架构范式。DRR利用深层精炼网络及非参数变换，在一次离线过程中将丰富表示编码为紧凑高效的嵌入结构。该方法将具有高表示能力的慢速神经网络与快速推理路径解耦。我们引入DRR-Net——一个验证该范式的简单网络，以及一种新型数据增强策略——变分对（VP），用于在复杂任务（如高维代理建模）中改进INR。在多个集成仿真数据集上的实验表明，我们的方法达到了最先进的真实度，同时在推理速度上比高真实度基线快高达27倍，且与最快模型保持竞争力。DRR范式为在更广泛应用中构建强大而实用的神经场代理和INR提供了一种有效策略，在速度与质量之间达成了最小折衷。