We present , an Artificial Intelligence (AI) multiagent system designed to serve as a scientific research assistant. can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting a scientific paper. The system has...
F. Villaescusa-Navarro, B. Bolliet, Pablo Villanueva-Domingo et al.· PRX Intelligence· 28 citations
We develop and validate a physics-informed neural network (PINN) framework for computing the tidal Love numbers of black holes directly from their perturbation equations. We consider static perturbations of the Kerr black hole, whose exact hypergeometric solutions provide a controlled benchmark for both the numerical s...
Maria J. Rodriguez, Luis Fernando Temoche, P. Protopapas· 0 citations
We present , an Artificial Intelligence (AI) multiagent system designed to serve as a scientific research assistant. can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting a scientific paper. The system has...
F. Villaescusa-Navarro, B. Bolliet, Pablo Villanueva-Domingo et al.· PRX Intelligence· 1 citation
In this work, we develop an inverse Physics-Informed Neural Network (PINN) framework to infer the dependence of the scrape-off layer (SOL) perpendicular heat conductivity on plasma density and temperature, $\kappa_\perp(n,T)$. The method combines radial profile measurements of electron density and temperature with the...
J. Gallego, P. Protopapas, A. de Bustos et al.· 0 citations
A physics-informed framework for learning finite-dimensional embeddings of solution families of partial differential equations using a multihead Physics-Informed Neural Network is introduced, which establishes the learned spectral profiles and principal components as robust observables of solution-manifold geometry.
Raúl Jiménez, S. Mayboroda, P. Protopapas et al.· arXiv.org· 0 citations
This work introduces a variational boosting framework in which solutions are constructed additively in function space and separates global nonlinear refinement into a sequence of well-conditioned subproblems while preserving the full variational structure of the operator.
P. Protopapas, K. Vo· arXiv.org· 0 citations
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