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Concept

Parameter space

Related publications (14)

An Easily Computable Error Estimator In Space And Time For The Wave Equation

Marco Picasso

We propose a cheaper version of a posteriori error estimator from Gorynina et al. (Namer. Anal. (2017)) for the linear second-order wave equation discretized by the Newmark scheme in time and by the finite element method in space. The new estimator preserv ...

EDP Sciences2019

Accurate and efficient interpretation of load-test data for asset-management

Sai Ganesh Sarvotham Pai

Increasing demand for new infrastructure and ageing of existing infrastructure has made management of infrastructure a key challenge of this century. Replacement of all ageing civil infrastructure is economically and environmentally unsustainable. Civil in ...

EPFL2019

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Gabriel Girard, Marco Pizzolato, Jonathan Rafael Patino Lopez, Thomas Yu

Probabilistic parameter estimation in model fitting runs the gamut from maximum likelihood or maximum a posteriori point estimates from optimization to Markov Chain Monte Carlo (MCMC) sampling. The latter, while more computationally intensive, generally pr ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

A taxonomy for Whole Building Life Cycle Assessment (WBLCA)

Catherine Elvire L. De Wolf

Purpose The purpose of this paper is to present an analysis of common parameters in existing tools that provide guidance to carry out Whole Building Life Cycle Assessment (WBLCA) and proposes a new taxonomy, a catalogue of parameters, for the definition of ...

EMERALD GROUP PUBLISHING LTD2019

Cosmological constraints from the convergence 1-point probability distribution

Jonathan Andrew Blazek

We examine the cosmological information available from the 1-point probability density function (PDF) of the weak-lensing convergence field, utilizing fast L-PICOLA simulations and a Fisher analysis. We find competitive constraints in the Omega(m)-sigma(8) ...

Oxford Univ Press2017

Approximate maximum likelihood estimation for population genetic inference

In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. Howev ...

De Gruyter2017

On the use of ANOVA expansions in reduced basis methods for high-dimensional parametric partial differential equations

Jan Sickmann Hesthaven

We propose two different improvements of reduced basis (RB) methods to enable the efficient and accurate evaluation of an output functional based on the numerical solution of parametrized partial differential equations with a possibly high-dimensional para ...

Springer Verlag2016

Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation

Giovanni De Micheli, Pierre-Emmanuel Julien Marc Gaillardon, Hassan Ghasemzadeh Mohammadi

With the growing number of process variation sources in deeply nano-scaled technologies, parameterized device and circuit modeling is becoming very important for chip design and verification. However, the high dimensionality of parameter space, for process ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Measuring the relative effect of factors affecting species distribution model predictions

Anthony Christopher Davison, Emeric Rolland Georges Thibaud

Species distribution models are increasingly used to address conservation questions, so their predictive capacity requires careful evaluation. Previous studies have shown how individual factors used in model construction can affect prediction. Although som ...

Wiley-Blackwell2014

A combination between the reduced basis method and the ANOVA expansion: On the computation of sensitivity indices

Gianluigi Rozza, Andrea Manzoni

We consider a method to efficiently evaluate in a real-time context an output based on the numerical solution of a partial differential equation depending on a large number of parameters. We state a result allowing to improve the computational performance ...

Elsevier2013

A generalization of the Solis-Wets method

In this paper we focus on the application of global stochastic optimization methods to extremum estimators. We propose a general stochastic method the master method which includes several stochastic optimization algorithms as a particular case. The propose ...

Elsevier2012

Quantitative Analysis of Robustness in Systems Biology : Combining Global and Local Approaches

Marc Hafner

To characterize the behavior and robustness of cellular circuits is a major challenge for Systems Biology. Many of the published methods that address this question quantify the local robustness of the models. In this thesis, I tried to underpin the inappro ...

EPFL2010

A new technique for high-resolution frequency domain optical coherence tomography

Michaël Unser, Thierry Blu

Frequency domain optical coherence tomography (FDOCT) is a new technique that is well-suited for fast imaging of biological specimens, as well as non-biological objects. The measurements are in the frequency domain, and the objective is to retrieve an arti ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2007

Numerical solution of parametrized Navier-Stokes equations by reduced basis methods

Alfio Quarteroni, Gianluigi Rozza

We apply the reduced basis method to solve Navier-Stokes equations in parametrized domains. Special attention is devoted to the treatment of the parametrized nonlinear transport term in the reduced basis framework, including the case of nonaffine parametri ...

2007