Functional Inverse Problems on Spheres: Theory, Algorithms and Applications

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Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Time-domain solutions of Maxwell’s equations in homogeneous and isotropic media are paramount to studying transient or broadband phenomena. However, analytical solutions are generally unavailable for practical applications, while numerical solutions are co ...

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In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

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Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

Functional Inverse Problems on Spheres: Theory, Algorithms and Applications

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Sparse autoregressive neural networks for classical spin systems

The time-domain Cartesian multipole expansion of electromagnetic fields

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Deep Learning Theory Through the Lens of Diagonal Linear Networks

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Digital Twins of Stone Masonry Buildings for Damage Assessment

Modeling friction and wear using an adaptive discrete-continuum coupling

Adaptive Finite Elements with Large Aspect Ratio. Application to Aluminium Electrolysis

Scalable constrained optimization