Carlos Sing Long

Carlos Sing Long

Carlos Sing Long cuenta con un Ph.D. in Computational and Mathematical Engineering de Stanford University, USA, obtenido en 2016. Previamente fue Investigador Principal en el Millennium Nucleus Center for the Discovery of Structures in Complex Data (MiDaS) y del Millennium Nucleus Center in Cardiovascular Magnetic Resonance (CardioMR). Su area de investigación es el análisis y resolución de problemas inversos discretos, con un énfasis en su aplicación en imágenes biomédicas. En CENIA, su investigación se enfoca en el uso de herramientas de inteligencia artificial en la simulación y aprendizaje de modelos de fenómenos físicos.

PUBLICACIONES

Publisher:  Computer Methods in Applied Mechanics and Engineering  Link>

ABSTRACT

Use of generative models and deep learning for physics-based systems is currently dominated by the task of emulation. However, the remarkable flexibility offered by data-driven architectures would suggest to extend this representation to other aspects of system analysis including model inversion and identifiability. We introduce InVAErt (pronounced invert) networks, a comprehensive framework for data-driven analysis and synthesis of parametric physical systems which uses a deterministic encoder and decoder to represent the forward and inverse solution maps, a normalizing flow to capture the probabilistic distribution of system outputs, and a variational encoder designed to learn a compact latent representation for the lack of bijectivity between inputs and outputs. We formally analyze how changes in the penalty coefficients affect the stationarity condition of the loss function, the phenomenon of posterior collapse, and propose strategies for latent space sampling, since we find that all these aspects significantly affect both training and testing performance. We verify our framework through extensive numerical examples, including simple linear, nonlinear, and periodic maps, dynamical systems, and spatio-temporal PDEs.

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