From the 1 of 7 linked papers with an AI index.
7 papers
Non-conformal obstructions to bubble expansion
David Mateos, Mikel Sanchez-Garitaonandia, Pedro Tarancón-Ãlvarez
The paper studies how non‑conformal thermodynamics affects the hydrodynamics of expanding bubbles in first‑order phase transitions, revealing new obstructions that limit bubble wal…
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…
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…
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 Denario project: Deep knowledge AI agents for scientific discovery
Francisco Villaescusa-Navarro, Boris Bolliet, Pablo Villanueva-Domingo +33
We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the…