GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems
Operator learning for partial differential equations (PDEs) on arbitrary geometries builds fast neural surrogates for large-scale simulation. Although recent geometry-adaptive neural operators have made substantial progress, they are mainly designed for forward problems in which inputs and outputs share the same spatial domain. This limits their applicability for boundary value problems (BVPs) and inverse problems, where inputs and outputs may live on different domains. We introduce the Geometry-Adaptive Integral Autoencoder (GAIA), an operator learning model that encodes the domain boundary a
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- LinkedLinked via arxiv author · 85%Meenakshi Krishnan →
“GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems”
- LinkedLinked via arxiv author · 85%Pranav Pulijala →
“GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems”
- LinkedLinked via arxiv author · 85%Ke Chen →
“GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems”
- LinkedLinked via arxiv author · 85%Haizhao Yang →
“GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems”
- LinkedLinked via arxiv author · 85%Ramani Duraiswami →
“GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems”
- PossiblePossibly related (embedding) · 56%Principled approaches for extending neural architectures to function spaces for operator learning →
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “GAIA: Geometry-Adaptive Operator Learning for Forward and In” ≈ “aymericdamien/TopDeepLearning””