8 citations · 16 across the 5 of their papers we have counts for
5 papers
Automated Classification of Plasma Regions at Mars Using Machine Learning
Yilan Qin, Chuanfei Dong, Hongyang Zhou +6
The plasma environment around Mars is highly variable because it is strongly influenced by the solar wind. Accurate identification of plasma regions around Mars is important for th…
Physics-Informed Neural Networks for Modeling the Martian Induced Magnetosphere
Jiawei Gao, Chuanfei Dong, Chi Zhang +6
Understanding the magnetic field environment around Mars and its response to upstream solar wind conditions provide key insights into the processes driving atmospheric ion escape.…
Surrogate Modeling of Landau Damping with Deep Operator Networks
Simin Shekarpaz, Chuanfei Dong, Ziyu Huang
Kinetic simulations excel at capturing microscale plasma physics phenomena with high accuracy, but their computational demands make them impractical for modeling large-scale space…
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
Simin Shekarpaz, Fanhai Zeng, George Karniadakis
We introduce a new approach for solving forward systems of differential equations using a combination of splitting methods and physics-informed neural networks (PINNs). The propose…
PIAT: Physics Informed Adversarial Training for Solving Partial Differential Equations
Simin Shekarpaz, Mohammad Azizmalayeri, Mohammad Hossein Rohban
In this paper, we propose the physics informed adversarial training (PIAT) of neural networks for solving nonlinear differential equations (NDE). It is well-known that the standard…