Author

A. S. Mancini

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Preprint Sep 2026

Differentiable astrophysics at scale: solving and differentiating ODE ensembles on the GPU

Astronomers increasingly fit their models with gradient-based methods, such as Hamiltonian Monte Carlo, which need the derivatives of the model with respect to its parameters. In many analyses a prediction requires solving a small system of ordinary differential equations (ODEs) for thousands to millions of parameter s...

A. S. Mancini · 1 citation

#machine learning Preprint Sep 2026

GRADSOLVE: fast exact gradients for ODE ensembles on GPUs

Ordinary differential equations (ODEs) underlie models in science and engineering, and many applications need derivatives of their solutions with respect to parameters. Ensembles of independent trajectories suit graphics processing units (GPUs), but current GPU software forces a trade-off: the fastest ensemble solvers...

A. S. Mancini · 2 citations