no code implementations • 2 Nov 2022 • Yilan Qin, Jiayu Ma, Mingle Jiang, Chuanfei Dong, Haiyang Fu, Liang Wang, Wenjie Cheng, YaQiu Jin
The multi-moment fluid model is trained with a small fraction of sparsely sampled data from kinetic simulations of Landau damping, using the physics-informed neural network (PINN) and the gradient-enhanced physics-informed neural network (gPINN).
no code implementations • 10 Sep 2022 • Wenjie Cheng, Haiyang Fu, Liang Wang, Chuanfei Dong, YaQiu Jin, Mingle Jiang, Jiayu Ma, Yilan Qin, Kexin Liu
The data-driven fluid modeling of PDEs for complex physical systems may be applied to improve fluid closure and reduce the computational cost of multi-scale modeling of global systems.
no code implementations • 21 Dec 2020 • James Green, Scott Boardsen, Chuanfei Dong
Characterizing habitable exoplanets and/or their moons is of paramount importance.
Earth and Planetary Astrophysics Space Physics
no code implementations • 5 Nov 2019 • Brian Nord, Andrew J. Connolly, Jamie Kinney, Jeremy Kubica, Gautaum Narayan, Joshua E. G. Peek, Chad Schafer, Erik J. Tollerud, Camille Avestruz, G. Jogesh Babu, Simon Birrer, Douglas Burke, João Caldeira, Douglas A. Caldwell, Joleen K. Carlberg, Yen-Chi Chen, Chuanfei Dong, Eric D. Feigelson, V. Zach Golkhou, Vinay Kashyap, T. S. Li, Thomas Loredo, Luisa Lucie-Smith, Kaisey S. Mandel, J. R. Martínez-Galarza, Adam A. Miller, Priyamvada Natarajan, Michelle Ntampaka, Andy Ptak, David Rapetti, Lior Shamir, Aneta Siemiginowska, Brigitta M. Sipőcz, Arfon M. Smith, Nhan Tran, Ricardo Vilalta, Lucianne M. Walkowicz, John ZuHone
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration.