8 papers
On the training of physics-informed neural operators for solving parametric partial differential equations
Nanxi Chen, Chuanjie Cui, Airong Chen +2
Physics-informed neural operators (PINOs) aim to learn solution operators for partial differential equations by using the governing physics as supervision, rather than relying sole…
CATO: Charted Attention for Neural PDE Operators
Chun-Wun Cheng, Sifan Wang, Carola-Bibiane Schönlieb +1
Neural operators have emerged as powerful data-driven solvers for PDEs, offering substantial acceleration over classical numerical methods. However, existing transformer-based oper…
The NANOGrav 15 yr Data Set: Piecewise Power-Law Reconstruction of the Gravitational-Wave Background
Gabriella Agazie, Akash Anumarlapudi, Anne M. Archibald +108
The NANOGrav 15-year (NG15) data set provides evidence for a gravitational-wave background (GWB) signal at nanohertz frequencies, which is expected to originate either from a cosmi…
FunDiff: Diffusion Models over Function Spaces for Physics-Informed Generative Modeling
Sifan Wang, Zehao Dou, Siming Shan +2
Recent advances in generative modeling -- particularly diffusion models and flow matching -- have achieved remarkable success in synthesizing discrete data such as images and video…
TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion
Zhikai Wu, Sifan Wang, Shiyang Zhang +5
Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynam…
GeoFunFlow: Geometric Function Flow Matching for Inverse Operator Learning over Complex Geometries
Sifan Wang, Zhikai Wu, David van Dijk +1
Inverse problems governed by partial differential equations (PDEs) are crucial in science and engineering. They are particularly challenging due to ill-posedness, data sparsity, an…