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Found 4 papers, 4 papers with code
$ρ$-Diffusion: A diffusion-based density estimation framework for computational physics
In physics, density $\rho(\cdot)$ is a fundamentally important scalar function to model, since it describes a scalar field or a probability density function that governs a physical process.
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 $N>2$.
Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation
Sampling-based inference techniques are central to modern cosmological data analysis; these methods, however, scale poorly with dimensionality and typically require approximate or intractable likelihoods.
