neural operators
Neural operators are advanced mathematical formulations used in the field of deep learning to learn mappings between function spaces, enhancing model generalizations for applications such as solving partial differential equations or other complex dynamics.
- AneuG-Flow: A Large-Scale Synthetic Dataset of Diverse Intracranial Aneurysm Geometries and Hemodynamics
- Boundary-Value PDEs Meet Higher-Order Differential Topology-aware GNNs
- FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation
- In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
- Infinite Neural Operators: Gaussian processes on functions
- Mixture-of-Experts Operator Transformer for Large-Scale PDE Pre-Training
- Neural Emulator Superiority: When Machine Learning for PDEs Surpasses its Training Data
- S-Crescendo: A Nested Transformer Weaving Framework for Scalable Nonlinear System in S-Domain Representation
- Stochastic Process Learning via Operator Flow Matching
- Uncertainty-Informed Meta Pseudo Labeling for Surrogate Modeling with Limited Labeled Data