FA-INR: Adaptive Implicit Neural Representations for Interpretable Exploration of Simulation Ensembles

ArXi:2506.06858v3 Announce Type: replace-cross 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 the 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.