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Found 6 papers, 6 papers with code
A hybrid approach for solving the gravitational N-body problem with Artificial Neural Networks
To increase the robustness of a method that uses neural networks, we propose a hybrid integrator that evaluates the prediction of the network and replaces it with the numerical solution if considered inaccurate.
Neural Symplectic Integrator with Hamiltonian Inductive Bias for the Gravitational $N$-body Problem
The gravitational $N$-body problem, which is fundamentally important in astrophysics to predict the motion of $N$ celestial bodies under the mutual gravity of each other, is usually solved numerically because there is no known general analytical solution for $N>2$.
The Ecological Impact of High-performance Computing in Astrophysics
The importance of computing in astronomy continues to increase, and so is its impact on the environment.
Instrumentation and Methods for Astrophysics Computational Physics Instrumentation and Detectors
Effects of stellar density on the photoevaporation of circumstellar discs
The stellar density of the region seems to influence the extent of the effects of external photoevaporation.
Earth and Planetary Astrophysics Astrophysics of Galaxies Solar and Stellar Astrophysics
Newton vs the machine: solving the chaotic three-body problem using deep neural networks
Since its formulation by Sir Isaac Newton, the problem of solving the equations of motion for three bodies under their own gravitational force has remained practically unsolved.
On the Reliability of N-body Simulations
A way to test this hypothesis is to make a direct comparison of an ensemble of solutions obtained by conventional methods with an ensemble of true solutions.
Instrumentation and Methods for Astrophysics
