Résumé : Many physical phenomena are modeled by parametrized PDEs. The poor knowledge on the involved parameters is often one of the numerous sources of uncertainties on these models. Some of these parameters can be estimated, with the use of real world data. The aim of this mini-symposium is to introduce some of the various tools from both statistical and numerical communities to deal with this issue. Parametric and non-parametric approaches are developed in this paper. Some of the estimation procedures require many evaluations of the initial model. Some interpolation tools and some greedy algorithms for model reduction are therefore also presented, in order to reduce time needed for running the model.no
In this paper, we use an industrial data set with an ordinary differential equation (ODE) model to d...
This paper addresses the development of a new algorithm for parameter estimation of ordinary differe...
We provide first the functional analysis background required for reduced order modeling and present ...
International audienceMany physical phenomena are modeled by parametrized PDEs. The poor knowledge o...
Many physical phenomena are modeled by parametrized PDEs. The poor knowledge on the involv...
Many physical phenomena are modeled by parametrized PDEs. The poor knowledge on the involv...
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in app...
A large variety of natural, industrial, and environmental systems involves phenomena that are contin...
Les systèmes décrits par les équations aux dérivées partielles, appartiennent à la classe des systèm...
International audienceOrdinary differential equations (ODE's) are widespread models in physics, chem...
Parameter estimation in non linear mixed effects models requires a large number of evaluations of th...
A large variety of natural, industrial, and environmental systems involves phenomena that are contin...
The problem of numerical least squares parameter estimation in differential equations is considered....
AbstractThe paper introduces a numerical method to estimate parameters in systems of one-dimensional...
In this paper, we use an industrial data set with an ordinary differential equation (ODE) model to d...
This paper addresses the development of a new algorithm for parameter estimation of ordinary differe...
We provide first the functional analysis background required for reduced order modeling and present ...
International audienceMany physical phenomena are modeled by parametrized PDEs. The poor knowledge o...
Many physical phenomena are modeled by parametrized PDEs. The poor knowledge on the involv...
Many physical phenomena are modeled by parametrized PDEs. The poor knowledge on the involv...
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in app...
A large variety of natural, industrial, and environmental systems involves phenomena that are contin...
Les systèmes décrits par les équations aux dérivées partielles, appartiennent à la classe des systèm...
International audienceOrdinary differential equations (ODE's) are widespread models in physics, chem...
Parameter estimation in non linear mixed effects models requires a large number of evaluations of th...
A large variety of natural, industrial, and environmental systems involves phenomena that are contin...
The problem of numerical least squares parameter estimation in differential equations is considered....
AbstractThe paper introduces a numerical method to estimate parameters in systems of one-dimensional...
In this paper, we use an industrial data set with an ordinary differential equation (ODE) model to d...
This paper addresses the development of a new algorithm for parameter estimation of ordinary differe...
We provide first the functional analysis background required for reduced order modeling and present ...