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Found 4 papers, 0 papers with code
Data-driven modeling of Landau damping by physics-informed neural networks
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).
Data-driven, multi-moment fluid modeling of Landau damping
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.
Magnetospheres of Terrestrial Exoplanets and Exomoons: Implications for Habitability and Detection
Characterizing habitable exoplanets and/or their moons is of paramount importance.
Earth and Planetary Astrophysics Space Physics
Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era
The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration.
