Transmission in Multimode fiber with deep learning

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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 ...

The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the e ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

We describe a novel method to compute the components of dynamo tensors from direct magnetohydrodynamic (MHD) simulations. Our method relies upon an extension and generalization of the standard H & ouml;gbom CLEAN algorithm widely used in radio astronomy to ...

Video DeepFakes are fake media created with Deep Learning (DL) that manipulate a person’s expression or identity. Most current DeepFake detection methods analyze each frame independently, ignoring inconsistencies and unnatural movements between frames. Som ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

The invention relates to methods for the preparation of method of preparation of Tau aggregates, including fibrils, fibrillar species, soluble and insoluble oligomeric species, new Tau aggregates and uses thereof. The invention further relates to the uses ...

Deep neural networks trained on physical losses are emerging as promising surrogates for nonlinear numerical solvers. These tools can predict solutions to Maxwell's equations and compute gradients of output fields with respect to the material and geometric ...

The finite element method is a well-established method for the numerical solution of partial differential equations (PDEs), both linear and nonlinear. However, the repeated re -assemblage of finite element matrices for nonlinear PDEs is frequently pointed ...

Transmission in Multimode fiber with deep learning

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

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Graph generative deep learning models with an application to circuit topologies

Iterative removal of sources to model the turbulent electromotive force

Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Method of preparation of amyloidogenic protein aggregates and uses thereof

Predicting nonlinear optical scattering with physics-driven neural networks

On the fast assemblage of finite element matrices with application to nonlinear heat transfer problems