3 citations · 6 across the 5 of their papers we have counts for
6 papers
Accelerating Black Hole Image Generation via Latent Space Diffusion Models
Ao Liu, Xudong Zhang, Lin Ding +3
Interpreting horizon-scale black hole images currently relies on computationally intensive General Relativistic Ray Tracing (GRRT) simulations, which pose a significant bottleneck…
Identifying Microlensing by Compact Dark Matter through Diffraction Patterns in Gravitational Waves with Machine Learning
Ao Liu, Tonghua Liu, Dejiang Li +5
Gravitational wave lensing, particularly microlensing by compact dark matter (DM), offers a unique avenue to probe the nature of dark matter. However, conventional detection method…
Identification of Strongly Lensed Gravitational Wave Events Using Squeeze-and-Excitation Multilayer Perceptron Data-efficient Image Transformer
Dejiang Li, Tonghua Liu, Ao Liu +4
With the advancement of third-generation gravitational wave detectors, the identification of strongly lensed gravitational wave (GW) events is expected to play an increasingly vita…
Physical parameter regression from black hole images via a multiscale adaptive neural network
Jialei Wei, Ao Liu, Dejiang Li +1
High-precision regression of physical parameters from black hole images generated by General Relativistic Ray Tracing (GRRT) is essential for investigating spacetime curvature and…
Advancing Cosmological Parameter Estimation and Hubble Parameter Reconstruction with Long Short-Term Memory and Efficient-Kolmogorov-Arnold Networks
Jiaxing Cui, Marek Biesiada, Ao Liu +3
In this work, we propose a novel approach for cosmological parameter estimation and Hubble parameter reconstruction using Long Short-Term Memory (LSTM) networks and Efficient-Kolmo…
Lean classical-quantum hybrid neural network model for image classification
Ao Liu, Cuihong Wen, Jieci Wang
The integration of algorithms from quantum information with neural networks has enabled unprecedented advancements in various domains. Nonetheless, the application of quantum machi…