FA-INR: Adaptive Implicit Neural Representations for Interpretable Exploration of Simulation Ensembles
Authors
Ziwei Li (The Ohio State University), Yuhan Duan (The Ohio State University), Tianyu Xiong (The Ohio State University), Yi-Tang Chen (The Ohio State University), Wei-Lun Chao (The Ohio State University), Han-Wei Shen (The Ohio State University)
Presentation
- Session
- Let me through, I'm a scientist!
- Time
- Tuesday, Nov 10, 15:24 – 15:36 (US/Eastern) · session 15:00 – 16:30
- Location
- Hall America north
Links
Sign in to access the preprint PDF.
Sign in
Keywords
Surrogate model, ensemble visualization, implicit neural representation, mixture-of-experts
Abstract
Surrogate models are essential for efficient exploration of large-scale ensemble simulations. Implicit neural representations (INRs) provide a compact and continuous framework for modeling spatially structured data, but they often struggle with learning complex localized structures within scientific fields. Recent INR-based surrogates address this by augmenting INRs with explicit feature structures, but at the cost of flexibility and substantial memory overhead. In this paper, we present Feature-Adaptive INR (FA-INR), an adaptive INR-based surrogate model for high-fidelity and interpretable exploration of ensemble simulations. Instead of relying on structured feature representations, FA-INR leverages cross-attention over a learnable key-value memory bank to allocate model capacity adaptively based on the data characteristics. To further improve scalability, we introduce a coordinate-guided mixture of experts (MoE) framework that enhances both efficiency and specialization of feature representations. More importantly, the learned experts produce an interpretable partition over the simulation domain, enabling scientists to identify complex structures and perform localized parameter-space exploration. Beyond quantitative and qualitative evaluations, we also demonstrate that our learned expert specialization can reveal meaningful scientific insights and support localized sensitivity analysis.
For Practitioners
simulation scientists