用元学习与聚类提升科学模拟数据的多变量编码效率
MC-INR: Efficient Encoding of Multivariate Scientific Simulation Data using Meta-Learning and Clustered Implicit Neural Representations
- 结合元学习与聚类，灵活表达复杂结构
- 在非规则网格上实现多变量数据高效压缩
- 适合处理真实世界复杂科学数据的科研人员
隐式神经表示（INRs）广泛用于将数据编码为连续函数，以降低大规模多变量科学模拟数据的内存占用并支持可视化。然而现有基于INR的方法存在三大局限：(1) 对复杂结构表达能力有限；(2) 主要针对单变量数据；(3) 依赖规则网格，导致在真实复杂数据上性能下降。为此，我们提出MC-INR框架，可在非规则网格上处理多变量数据。该方法融合元学习与聚类，实现复杂结构的灵活编码。为进一步提升性能，引入基于残差的动态重聚类机制，根据局部误差自适应划分聚类；同时设计分支层，通过独立分支并行利用多变量信息。实验表明，MC-INR在科学数据编码任务中优于现有方法。
原文摘要 · Abstract (English)
Implicit Neural Representations (INRs) are widely used to encode data as continuous functions, enabling the visualization of large-scale multivariate scientific simulation data with reduced memory usage. However, existing INR-based methods face three main limitations: (1) inflexible representation of complex structures, (2) primarily focusing on single-variable data, and (3) dependence on structured grids. Thus, their performance degrades when applied to complex real-world datasets. To address these limitations, we propose a novel neural network-based framework, MC-INR, which handles multivariate data on unstructured grids. It combines meta-learning and clustering to enable flexible encoding of complex structures. To further improve performance, we introduce a residual-based dynamic re-clustering mechanism that adaptively partitions clusters based on local error. We also propose a branched layer to leverage multivariate data through independent branches simultaneously. Experimental results demonstrate that MC-INR outperforms existing methods on scientific data encoding tasks.
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