Title: Explorable INR: An Implicit Neural Representation for Ensemble Simulation Enabling Efficient Spatial and Parameter Exploration Show Chinese title

Title: 可探索的隐式神经表示：一种启用高效空间和参数探索的集合模拟隐式神经表示

Abstract: With the growing computational power available for high-resolution ensemble simulations in scientific fields such as cosmology and oceanology, storage and computational demands present significant challenges. Current surrogate models fall short in the flexibility of point- or region-based predictions as the entire field reconstruction is required for each parameter setting, hence hindering the efficiency of parameter space exploration. Limitations exist in capturing physical attribute distributions and pinpointing optimal parameter configurations. In this work, we propose Explorable INR, a novel implicit neural representation-based surrogate model, designed to facilitate exploration and allow point-based spatial queries without computing full-scale field data. In addition, to further address computational bottlenecks of spatial exploration, we utilize probabilistic affine forms (PAFs) for uncertainty propagation through Explorable INR to obtain statistical summaries, facilitating various ensemble analysis and visualization tasks that are expensive with existing models. Furthermore, we reformulate the parameter exploration problem as optimization tasks using gradient descent and KL divergence minimization that ensures scalability. We demonstrate that the Explorable INR with the proposed approach for spatial and parameter exploration can significantly reduce computation and memory costs while providing effective ensemble analysis. Show Chinese abstract

Abstract: 随着宇宙学和海洋学等科学领域高分辨率集合模拟的计算能力不断增强，存储和计算需求带来了显著挑战。 现有的代理模型在参数设置下需要整个场重建，从而在基于点或区域的预测灵活性方面表现不足，这阻碍了参数空间探索的效率。 这些模型在捕捉物理属性分布和确定最佳参数配置方面存在局限性。 在这项工作中，我们提出了可探索的INR（隐式神经表示）——一种新型基于隐式神经表示的代理模型，旨在促进探索并允许基于点的空间查询，而无需计算完整的场数据。 此外，为了进一步解决空间探索中的计算瓶颈，我们利用概率仿射形式（PAF）通过可探索的INR进行不确定性传播，以获得统计摘要，从而促进各种昂贵的现有模型的集合分析和可视化任务。 此外，我们将参数探索问题重新表述为使用梯度下降和KL散度最小化的优化任务，确保其可扩展性。 我们证明了使用所提出的方法进行空间和参数探索的可探索INR可以在提供有效的集合分析的同时显著降低计算和内存成本。