6 papers
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…
Recovering Sharp Conductivity Features in the Finite-Data Calderón Problem with Physics-Informed Neural Networks
Ali AlHadi Kalout, Pablo Tejerina-Pérez, Konstantin Karchev +5
Physics-informed neural networks (PINNs) have recently emerged as a promising framework for addressing the Calderón inverse problem from limited boundary data. In this work, we re…
A Bell experiment during inflation: probing quantum entanglement in tensor fluctuations through correlations of primordial scalar curvature perturbations
Pablo Tejerina-Pérez, Leonid Sarieddine, Daniele Bertacca +1
We propose a method that provides an observational signature of the quantum origin of primordial fluctuations generated during inflation. The method gives a prescription for testin…
A Bell Experiment in an Entangled Universe
Pablo Tejerina-Pérez, Daniele Bertacca, Raul Jimenez +1
We propose a possible quantum signature of the early Universe that could lead to observational imprints of the quantum nature of the inflationary period. Graviton production from t…
Learning embeddings of non-linear PDEs: the Burgers' equation
Pedro Tarancón-Ãlvarez, Leonid Sarieddine, Pavlos Protopapas +1
Embeddings provide low-dimensional representations that organize complex function spaces and support generalization. They provide a geometric representation that supports efficient…
The Atacama Cosmology Telescope: Constraints on Local Non-Gaussianity from the ACT Cluster Catalog
Leonid Sarieddine, J. Richard Bond, Matt Hilton +8
We derive constraints on local-type primordial non-Gaussianity using the ACT DR6 Sunyaev--Zel'dovich cluster catalog. Modeling the redshift- and mass-dependent number counts of 1,2…