196 citations · 2.8k across the 144 of their papers we have counts for
148 papers
Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices
J. Gallego, P. Protopapas, A. Bustos +6
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…
On the performance of pre-trained vision transformers for supernova spectral classification using different spectral representations
J. Serrano Bell, P. Gálvez Molina, V. Contreras Rojas +4
The spectroscopic classification of supernovae is a key component of time-domain astronomy and plays an important role in the identification of Type Ia events. The increasing volum…
Variational Boosting for Physics-Informed Neural Networks
Pavlos Protopapas, Kaylee Vo
Physics-Informed Neural Networks (PINNs) solve differential equations by minimizing the residual of a nonlinear operator over a neural parameterization of the solution. However, mo…
AGNFormer I: Reconstruction of AGN spectra using a probabilistic transformer model
Benedict L. Rouse, Franz E. Bauer, Tomasz Różański +8
We explore how an uncertainty-aware transformer-based architecture can leverage information embedded across the entire observed optical spectra of AGN, focusing on the algorithm's…
Physics-Informed Neural Embeddings of PDE Solution Families
Raul Jimenez, Svitlana Mayboroda, Pavlos Protopapas +3
We introduce a physics-informed framework for learning finite-dimensional embeddings of solution families of partial differential equations. The method uses a multihead Physics-Inf…
Gravitational Duals from Equations of State II: Large Hierarchies and False Vacua
Raul Jimenez, David Mateos, Pavlos Protopapas +3
We investigate the reconstruction of holographic duals for strongly coupled quantum field theories in regimes characterized by large hierarchies and the presence of false vacua. Wi…