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Optimization framework for calibration of constitutive models enhanced by neural networks

Publications associées (34)

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Numerical modeling and neural networks to identify constitutive parameters from in situ tests

Rafal Filip Obrzud

This study proposes a new advanced algorithm for determining material parameters based on in situ tests. In situ testing gives an opportunity to perform soil characterization in natural stress conditions on a representative soil mass. Most field techniques ...

EPFL2009

A combined neural network/gradient-based approach for the identification of constitutive model parameters using self-boring pressuremeter tests

Laurent Vulliet, Rafal Filip Obrzud

This paper presents a numerical procedure of material parameter identification for the coupled hydromechanical boundary value problem (BVP) of the self-boring pressuremeter test (SBPT) in clay. First, the neural network (NN) technique is applied to obtain ...

2009

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

Perspectives of the high-dimensional dynamics of neural microcircuits from the point of view of low-dimensional readouts

Henry Markram

We investigate generic models for cortical microcircuits, i.e., recurrent circuits of integrate-and-fire neurons with dynamic synapses. These complex dynamic systems subserve the amazing information processing capabilities of the cortex, but are at the pre ...

2003

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000

Mixture Models for Unsupervised and Supervised Learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of all this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

IDIAP2000

Mixture Models for Unsupervised and Supervised Learning

École Polytechnique Fédérale de Lausanne, Computer Science Department2000

High Order and Multilayer Perceptron Initialization

Proper initialization is one of the most important prerequisites for fast convergence of feed-forward neural networks like high order and multilayer perceptrons. This publication aims at determining the optimal variance (or range) for the initial weights a ...

IEEE1997

Hardware-Friendly Learning Algorithms for Neural Networks: An Overview

The hardware implementation of artificial neural networks and their learning algorithms is a fascinating area of research with far-reaching applications. However, the mapping from an ideal mathematical model to compact and reliable hardware is far from evi ...

IEEE Computer Society Press1996

Image Classification by Neural Networks for the Quality Control of Watches

A method is presented for the automatic time detection of watches, where the hands are classified by a neural network. In order to reduce the overall cost of data collection, strict limits were imposed on the data collection time. This constraint severely ...

IDIAP1996