Proposes Interface Autoencoder (IAE) for learning neural operators on geometry-dependent PDEs, representing interface-function states in a fixed domain via finite projection codes.
A joint training approach that addresses neural operator out-of-distribution degradation and PINN multi-solution convergence errors by leveraging imperfect neural operators to guide PINN optimization.
Proposes an adaptive memory gating mechanism enabling neural operators to dynamically adjust memory weights based on observation conditions, improving adaptability in solving time-dependent PDEs.
See LICENSE fileNeural OperatorsAdaptive MemoryTime-dependent PDE
A Transformer-based neural operator approach for 3D wind field prediction over complex mountainous terrain, addressing bottlenecks in traditional CFD methods that require expert-intensive mesh generation and computationally expensive iterative solvers.
See LICENSE fileNeural OperatorsWind Field PredictionTransformer
A fundamental physics neural operator model project published by Delta Lab on HuggingFace. Refer to official repository documentation for specific functionality and performance details.
HIPNO leverages symmetry-aware physics-informed neural operators to infer hemodynamic states from non-invasive signals, addressing scale symmetry challenges in physics-informed hemodynamic inference and expanding access to advanced monitoring.
See LICENSE fileHemodynamicsPhysics-Informed Neural NetworksNeural Operators
PhD course lecture notes from University of Bozen/Bolzano covering Physics Informed Neural Networks and Neural Operators with PyTorch implementation tutorials.
