Publication

Comparison of dynamic load modeling using neural network and traditional method

Publications associées (51)

Learning new physics from a machine

We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The virtues of neural networks as unbiased function approximants make them particu ...

AMER PHYSICAL SOC2019

Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

Jan Sickmann Hesthaven

We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled porovis ...

2018

Non-intrusive reduced order modeling of nonlinear problems using neural networks

Jan Sickmann Hesthaven, Stefano Ubbiali

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and emplo ...

2018

A data-driven shock capturing approach for discontinuous Galekin methods

Jan Sickmann Hesthaven

We propose a data-driven artificial viscosity model for shock capturing in discontinuous Galerkin methods. The proposed model trains a multi-layer feedforward network to map from the element-wise solution to a smoothness indicator, based on which the artif ...

2018

A neural approach to drugs monitoring for personalized medicine

Giovanni De Micheli, Sandro Carrara, Camilla Baj-Rossi, Marius Schirmer

The development of fast and mobile drug detection is an important aspect of personalized medicine. It enables the quick assessment of inter-individual differences in drug metabolism and corresponding adjustments of the dose. Recent developments of amperome ...

2015

Numerical modeling and neural networks to identify model parameters from piezocone tests: II. Multi-parameter identification from piezocone data

Laurent Vulliet, Rafal Filip Obrzud, Andrzej Truty

This paper completes the study presented in the accompanying paper, and demonstrates a numerical algorithm for parameter prediction from the piezocone test (CPTU) data. This part deals with a development of neural network (NN) models which are able to map ...

Wiley-Blackwell2012

Definitive Consensus for Distributed Data Inference

Leonidas Georgopoulos

Inference from data is of key importance in many applications of informatics. The current trend in performing such a task of inference from data is to utilise machine learning algorithms. Moreover, in many applications that it is either required or is pref ...

EPFL2011

Reaction Enthalpies Using the Neural-Network-Based X1 Approach: The Important Choice of Input Descriptors

Matthew Wodrich

Artificial neural networks represent a simple but efficient way to model and correct known errors existing between commonly used density functional computations and experimental data. The recently proposed X1 approach combines B3LYP energies with a neural- ...

2009

Application of Artificial Neural Network to Predict Survival Time for Patients with Bladder Cancer

Rafal Tomasz Dlugosz

This paper presents an application of an artificial neural network to determine survival time of patients with a bladder cancer. Different learning methods have been investigated to find a solution, which is most optimal from a computational complexity poi ...

Springer-Verlag2009

Optimization framework for calibration of constitutive models enhanced by neural networks

Laurent Vulliet, Rafal Filip Obrzud

A two-level procedure designed for the estimation of constitutive model parameters is presented in this paper. The neural network (NN) approach at the first level is applied to achieve the first approximation of parameters. This technique is used to avoid ...

2009

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