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…
GeoFunFlow: Geometric function flow matching for joint probabilistic inference of physical fields and complex geometries
Sifan Wang, Yajie Ji, Zhikai Wu +2
Inverse problems governed by partial differential equations (PDEs) arise widely in science and engineering, but are often ill-posed and limited by sparse, noisy observations. In ma…
Neural Networks as Surrogate Solvers for Time-Dependent Accretion Disk Dynamics
Shunyuan Mao, Weiqi Wang, Sifan Wang +7
Accretion disks are ubiquitous in astrophysics, appearing in diverse environments from planet-forming systems to X-ray binaries and active galactic nuclei. Traditionally, modeling…
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…
Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution
Wuzhe Xu, Yulong Lu, Sifan Wang +1
We propose a unified diffusion model-based correction and super-resolution method to enhance the fidelity and resolution of diverse low-quality data through a two-step pipeline. Fi…
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…