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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