66 citations · 68 across the 2 of their papers we have counts for
2 papers
astro-ph.GA2023★ 2 cited
Accelerating galaxy dynamical modeling using a neural network for joint lensing and kinematics analyses
Matthew R. Gomer, Sebastian Ertl, Luca Biggio +6
Strong gravitational lensing is a powerful tool to provide constraints on galaxy mass distributions and cosmological parameters, such as the Hubble constant, . Nevertheless, i…
math.NA2022★ 66 cited
Pre-training strategy for solving evolution equations based on physics-informed neural networks
Jiawei Guo, Yanzhong Yao, Han Wang +1
The physics informed neural network (PINN) is a promising method for solving time-evolution partial differential equations (PDEs). However, the standard PINN method may fail to sol…