Self-explainable Operator Learning for Discovering Spatial Patterns in Functional Data
Operator learning has emerged as a powerful tool for modeling complex physical systems in functional spaces. However, their neural network-based architectures make them opaque models, obscuring the reasoning behind their predictions. In this work, we introduce a self-explainable operator learning framework that overcomes this challenge by reformulating operator learning as a linear combination of generalized functional linear models expressed through integral equations. Exploiting the additive decomposability of these integral equations, we divide the input domain into subdomains and compute l
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- LinkedLinked via arxiv author · 85%Mojgan Alishiri →
“Self-explainable Operator Learning for Discovering Spatial Patterns in Functional Data”
- LinkedLinked via arxiv author · 85%Amirhossein Arzani →
“Self-explainable Operator Learning for Discovering Spatial Patterns in Functional Data”
- PossiblePossibly related (embedding) · 64%Principled approaches for extending neural architectures to function spaces for operator learning →
- PossiblePossibly related (embedding) · 52%Enabling local neural operators to perform equation-free system-level analysis →
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Self-explainable Operator Learning for Discovering Spatial P” ≈ “aymericdamien/TopDeepLearning””