3 papers
physics.comp-ph2025
Physics-Informed Neural Networks with Dynamical Boundary Constraints
Andrés MartÃnez-Esteban, Pablo Calvo-Barlés, Luis MartÃn-Moreno +1
Physics-informed neural networks (PINNs) are numerical solvers that embed all the physical information of a system into the loss function of a neural network. In this way the learn…
cond-mat.mtrl-sci2025
Roadmap for Photonics with 2D Materials
F. Javier GarcÃa de Abajo, D. N. Basov, Frank H. L. Koppens +145
Triggered by the development of exfoliation and the identification of a wide range of extraordinary physical properties in self-standing films consisting of one or few atomic layer…
physics.comp-ph2025
Learning finite symmetry groups of dynamical systems via equivariance detection
Pablo Calvo-Barlés, Sergio G. Rodrigo, Luis MartÃn-Moreno
In this work, we introduce the Equivariance Seeker Model (ESM), a data-driven method for discovering the underlying finite equivariant symmetry group of an arbitrary function. ESM…