The paper introduces SINFONIA, a family of neural-flow architectures designed to learn finite-time evolution maps for orbital dynamics while preserving key physical structures. Three variants—symplectic/slimplectic, Taylor-anchored, and Magnusian—are applied to a 2.5PN neutron-star inspiral, demonstrating that long-term accuracy depends on a single secular channel tied to energy–angular-momentum balance rather than pointwise errors. These learned maps achieve accurate phase evolution over up to $10^{5}$ orbital periods at lower computational cost than traditional integrators, and can also be used to infer unmodeled forces from accumulated phase data.
The paper introduces SINFONIA, a family of neural‑flow architectures designed to learn structure‑preserving evolution maps for long‑duration gravitational‑wave modelling. Three variants—symplectic, slimplectic, and Magnusian—are trained on a 2.5PN neutron‑star inspiral and demonstrate that long‑time accuracy is governed by a single secular channel linked to energy–angular‑momentum balance, allowing accurate integration over up to $10^{5}$ orbital periods with far fewer computational steps than traditional integrators. The learned maps also enable physics inference, recovering un‑modelled dissipative forces from accumulated phase information.
By Lidia J. Gomes Da Silva
NucleoNet is a neural‑network variational Monte Carlo ansatz that treats electrons and nuclei as a unified particle set, allowing a single model to be optimized across multiple geometries. The approach uses a shared electron–nucleus backbone to evolve one‑particle and two‑particle representations, with nuclear streams learning representations that condition orbital construction while antisymmetry is enforced only by electronic determinants. On small‑molecule benchmarks, NucleoNet matches the accuracy of established neural‑wavefunction baselines with far fewer parameters and successfully describes dissociation curves and minimum‑energy paths for several systems, demonstrating that a compact wavefunction can capture molecular geometries with a single set of parameters.
By Qiao Gao, Yang Huang, Enze Hou, Yuzhi Liu, Jingrun Chen, Han Wang
The article discusses how machine learning can be applied to the Sun’s interior and atmosphere, which are complex, multiscale dynamical systems. It introduces a state-space framework where the Sun’s global state vector is governed by thermodynamic, kinetic, and magnetic properties, and observations provide only partial, projected measurements. The authors propose that machine learning serves as a family of operators—Inference, Emulation, Propagation, and Control—to reconstruct, simulate, and couple this hidden state across different physical layers, thereby bridging observational gaps and accelerating nonlinear simulations to predict the Sun’s global evolution.
By Vishal Upendran, Carlos J. D\'iaz Baso
arXiv:2606. 30936v1 Announce Type: cross Abstract: Generative Astrodynamics is advanced in this work by extending generative modelling to an orbit determination problem in the cislunar environment.
By Walther Litteri, Massimiliano Vasile
ASTROLABE is a flow‑matching generative emulator that operates directly on the 6N phase‑space coordinates of individual particles, avoiding voxel grids and allowing any observable to be measured from inference. It uses a field‑particle hybrid transformer so that compute and memory scale linearly with the number of particles, and incorporates physical flow matching by basing the prior on COLA‑derived coordinates with momentum and mass conservation enforced throughout the flow. In Lambda‑CDM tests, ASTROLABE reproduces realistic fields, accurate power spectra, halo mass functions, velocity profiles, and halo density profiles, and it generalizes to out‑of‑distribution dark‑acoustic oscillation initial conditions while also being fine‑tuned to emulate large‑scale simulations such as TNG50‑3‑dark at a fraction of the cost.
By Ibrahim Elsharkawy, David Curtin, Yonatan Kahn, Neal Weiner