Observations of neutron star mergers have the potential to unveil detailed physics of matter
and gravity in regimes inaccessible by other experiments. Quantitative comparisons to …

Neutrinos propagating in a dense neutrino gas, such as those expected in core-collapse
supernovae (CCSNe) and neutron star mergers (NSMs), can experience fast flavor …

Substantial effort has been devoted to the characterization of transient phenomena from
photometric information. Automated approaches to this problem have taken advantage of …

The modeling of multi-scale and multi-physics complex systems typically involves the use of
scientific software that can optimally leverage extreme scale computing. Despite major …

ABSTRACT In this study, Physics-Informed Neural Networks (PINNs) are skilfully applied to
explore a diverse range of pulsar magnetospheric models, specifically focusing on …

In this paper, we propose a generic algorithm to train machine learning-based subgrid
parametrizations online, ie, with a posteriori loss functions, but for non-differentiable …

We use artificial intelligence (AI) to learn and infer the physics of higher order gravitational
wave modes of quasi-circular, spinning, non precessing binary black hole mergers. We …

The convergence of physics-informed and machine learning has led to the emergence of
Physics-Informed Machine Learning (PIML), a powerful paradigm to enhance the reliability …

The turbulent aftermath of a neutron star collision | Nature Astronomy Skip to main content
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Einstein field equations are notoriously challenging to solve due to their complex
mathematical form, with few analytical solutions available in the absence of highly symmetric …