3 papers
cs.LG2026
Derived Fields Preserve Fine-Scale Detail in Budgeted Neural Simulators
Wenshuo Wang, Fan Zhang
Fine-scale-faithful neural simulation under fixed storage budgets remains challenging. Many existing methods reduce high-frequency error by improving architectures, training object…
gr-qc2024
Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach
Xihua Zhu, Yiqian Yang, Fan Zhang
With the rapid development of gravitational wave astronomy, the increasing number of detected events necessitates efficient methods for parameter estimation and model updates. This…
cs.LG2024
Adaptive Epsilon Adversarial Training for Robust Gravitational Wave Parameter Estimation Using Normalizing Flows
Yiqian Yang, Xihua Zhu, Fan Zhang
Adversarial training with Normalizing Flow (NF) models is an emerging research area aimed at improving model robustness through adversarial samples. In this study, we focus on appl…