1 citations · 1 across the 7 of their papers we have counts for
30 papers
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 RóżaÅski +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…
ABC-SN: Attention Based Classifier for Supernova Spectra
Willow Fox Fortino, Federica B. Bianco, Pavlos Protopapas +2
While significant advances have been made in photometric classification ahead of the millions of transient events and hundreds of supernovae (SNe) each night that the Vera C. Rubin…
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…
Identifying Observational Signatures of Flux Eruption Events in Supermassive Black Hole Accretion Flows with Machine Learning
Angelo Ricarte, Erandi Chavez, Franc O +1
Simulated black hole accretion flows with strong magnetic fields often exhibit "flux eruption events" (FEEs), transient and localized expulsions of matter near the event horizon du…