# Neural Symplectic Integrator with Hamiltonian Inductive Bias for the Gravitational $N$-body Problem

@article{Cai2021NeuralSI, title={Neural Symplectic Integrator with Hamiltonian Inductive Bias for the Gravitational \$N\$-body Problem}, author={Maxwell Xu Cai and Simon Portegies Zwart and Damian Podareanu}, journal={ArXiv}, year={2021}, volume={abs/2111.15631} }

The gravitational N -body problem, which is fundamentally important in astrophysics to predict the motion ofN celestial bodies under the mutual gravity of each other, is usually solved numerically because there is no known general analytical solution for N > 2. Can an N -body problem be solved accurately by a neural network (NN)? Can a NN observe long-term conservation of energy and orbital angular momentum? Inspired by Wistom & Holman’s symplectic map, we present a neural N -body integrator…

## One Citation

### Learning Neural Hamiltonian Dynamics: A Methodological Overview

- Computer ScienceArXiv
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This paper systematically survey recently proposed Hamiltonian neural network models, with a special emphasis on methodologies, and discusses the major contributions of these models in four overlapping directions.

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