Semianalytical approach to the design of photonic crystal cavities

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Parametric Wavelength Conversion in Waveguiding Structures: Efficiency, Bandwidth and Polarization

Arman Ayan

Photonics integrated circuits are a promising solution for the growing demands of data transmission and future system-on-chip technologies. Within this context, nonlinear optical interactions offer unique opportunities for all-optical processing, sampling, ...

EPFL2024

Silicon nitride electric-field poled microresonator modulator

Camille Sophie Brès, Anton Stroganov, Boris Zabelich, Christian André Clément Lafforgue, Edgars Nitiss

Stoichiometric silicon nitride is a highly regarded platform for its favorable attributes, such as low propagation loss and compatibility with complementary metal-oxide-semiconductor technology, making it a prominent choice for various linear and nonlinear ...

2024

Towards Trustworthy Deep Learning for Image Reconstruction

Alexis Marie Frederic Goujon

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

EPFL2024

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Aude Billard, Nadia Barbara Figueroa Fernandez, Mikhail Koptev

Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is construc ...

Sage Publications Ltd2024

Extensions of Peer Prediction Incentive Mechanisms

Adam Julian Richardson

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

EPFL2024

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Ksenia Briling

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

EPFL2024

Dynamical Low-Rank Approximation for Stochastic Differential Equations

Fabio Nobile, Yoshihito Kazashi, Fabio Zoccolan

In this paper, we set the mathematical foundations of the Dynamical Low-Rank Approximation (DLRA) method for stochastic differential equations (SDEs). DLRA aims at approximating the solution as a linear combination of a small number of basis vectors with r ...

American Mathematical Society (AMS)2024

Density estimation in RKHS with application to Korobov spaces in high dimensions

Fabio Nobile, Yoshihito Kazashi

A kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined b ...

2023

Get the Best of Both Worlds: Improving Accuracy and Transferability by Grassmann Class Representation

Haoqi Wang, Zhigang Li

We generalize the class vectors found in neural networks to linear subspaces (i.e., points in the Grassmann manifold) and show that the Grassmann Class Representation (GCR) enables simultaneous improvement in accuracy and feature transferability. In GCR, e ...

Ieee Computer Soc2023

Metasurface-stabilized optical microcavities

Ileana-Cristina Benea-Chelmus

Cavities concentrate light and enhance its interaction with matter. Confining to microscopic volumes is necessary for many applications but space constraints in such cavities limit the design freedom. Here we demonstrate stable optical microcavities by cou ...

Nature Portfolio2023

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