Concept

Nonlinear system identification

Related publications (84)

Controllability and Interpretability in Affective Speech Synthesis

Thanks to Deep Learning Text-To-Speech (TTS) has achieved high audio quality with large databases. But at the same time the complex models lost any ability to control or interpret the generation process. For the big challenge of affective TTS it is infeasi ...

EPFL2022

Mesoscopic modeling of hidden spiking neurons

Wulfram Gerstner, Shuqi Wang, Valentin Marc Schmutz

Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved? Modeling the unobserved neurons with large pools of hidden spiking neurons leads to severely underconstr ...

2022

Light detection and ranging with entangled photons

Edoardo Charbon, Jiuxuan Zhao, Arin Can Ülkü

Single-photon light detection and ranging (LiDAR) is a key technology for depth imaging through complex environments. Despite recent advances, an open challenge is the ability to isolate the LiDAR signal from other spurious sources including background lig ...

OPTICAL SOC AMER2022

Neural network surrogate of QuaLiKiz using JET experimental data to populate training space

Henri Weisen, Yann Camenen

Within integrated tokamak plasma modeling, turbulent transport codes are typically the computational bottleneck limiting their routine use outside of post-discharge analysis. Neural network (NN) surrogates have been used to accelerate these calculations wh ...

AMER INST PHYSICS2021

Prediction of the reaction forces of spiral-groove gas journal bearings by artificial neural network regression models

Jürg Alexander Schiffmann, Elia Iseli

This paper presents neural network regression models for predicting the nonlinear static and linearized dynamic reaction forces of spiral grooved gas journal bearings. The partial differential equations (PDEs) are sampled, based on a full factorial and ran ...

2021

POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium

Alfio Quarteroni, Andrea Manzoni

The numerical simulation of multiple scenarios easily becomes computationally prohibitive for cardiac electrophysiology (EP) problems if relying on usual high-fidelity, full order models (FOMs). Likewise, the use of traditional reduced order models (ROMs) ...

FRONTIERS MEDIA SA2021

Uncertainty-aware Joint Salient Object and Camouflaged Object Detection

Tong Zhang

Visual salient object detection (SOD) aims at finding the salient object(s) that attract human attention, while camouflaged object detection (COD) on the contrary intends to discover the camouflaged object(s) that hidden in the surrounding. In this paper, ...

IEEE COMPUTER SOC2021

Physics-informed machine learning for reduced-order modeling of nonlinear problems

Qian Wang, Jan Sickmann Hesthaven

A reduced basis method based on a physics-informed machine learning framework is developed for efficient reduced-order modeling of parametrized partial differential equations (PDEs). A feedforward neural network is used to approximate the mapping from the ...

2021

Learning Activation Functions in Deep (Spline) Neural Networks

Michaël Unser, Pakshal Narendra Bohra, Shayan Aziznejad, Harshit Gupta, Joaquim Gonçalves Garcia Barreto Campos

We develop an efficient computational solution to train deep neural networks (DNN) with free-form activation functions. To make the problem well-posed, we augment the cost functional of the DNN by adding an appropriate shape regularization: the sum of the ...

2020

Dynamic Model Pruning with Feedback

Martin Jaggi, Sebastian Urban Stich, Luis Felipe Barba Flores, Tao Lin, Daniil Dmitriev

Deep neural networks often have millions of parameters. This can hinder their deployment to low-end devices, not only due to high memory requirements but also because of increased latency at inference. We propose a novel model compression method that gener ...

2020

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