Publications related to Surrogate model

Self-learning surrogate models in superstructure optimization

François Maréchal, Ivan Daniel Kantor, Julia Granacher, Michel Lopez

In this contribution, we propose an algorithm for replacing non-linear process simulation integrated in multi-level optimization of an energy system superstructure with surrogate models. With our approach, we demonstrate that surrogate models are a valid t ...

Elsevier2021

Competing Adaptive Networks

Ali H. Sayed, Stefan Vlaski

Adaptive networks have the capability to pursue solutions of global stochastic optimization problems by relying only local interactions within neighborhoods. The diffusion of information through repeated interactions allows for globally optimal behavior, w ...

IEEE2021

Optimization Methods for Control: From Embedded Programmable Hardware to Data-Driven Process Optimization

Harsh Ambarishkumar Shukla

The research community has been making significant progress in hardware implementation, numerical computing and algorithm development for optimization-based control. However, there are two key challenges that still have to be overcome for optimization-base ...

EPFL2021

Aerodynamic shape optimization via surrogate modelling with convolutional neural networks

Francesco Bardi

Aerodynamic shape optimization has become of primary importance for the aerospace industry over the last years. Most of the method developed so far have been shown to be either computationally very expensive, or to have low dimensional search space. In thi ...

2019

Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Interpolation Approach

Colin Neil Jones, Emilio Maddalena

We propose a non-parametric regression method that does not rely on the structure of the ground-truth, but only on its regularity properties. The methodology can be readily used for learning surrogate models of nonlinear dynamical systems from data, while ...

2019

Machine learning methods to assist energy system optimization

Jean-Louis Scartezzini, Amarasinghage Tharindu Dasun Perera, Vahid Moussavi Nik

This study evaluates the potential of supervised and transfer learning techniques to assist energy system optimization. A surrogate model is developed with the support of a supervised learning technique (by using artificial neural network) in order to bypa ...

2019

On the Use of Transient Information for Static Real-Time Optimization

Dominique Bonvin, Grégory François, Alejandro Gabriel Marchetti

Optimal operation of chemical processes is key for meeting productivity, quality, safety and environmental objectives. Both model-based and data-driven schemes are used to compute optimal operating conditions [1]: - The model-based techniques are intu ...

2015

Integrating Rate Based Models into a Multi-Objective Process Design & Optimisation Framework using Surrogate Models

Levent Sinan Teske

In the development of energy and chemical processes, the process engineers extensively apply computer aided methods to design & optimise these processes and corresponding process units. Such applications are multi-scale modelling and multi-objective optimi ...

EPFL2014

Analysis of the finite element heterogeneous multiscale method for quasilinear elliptic homogenization problems

Assyr Abdulle, Gilles Vilmart

A fully discrete analysis of the finite element heterogeneous multiscale method for a class of nonlinear elliptic homogenization problems of nonmonotone type is proposed. In contrast to previous results obtained for such problems in dimension

d\leq2

for ...

Amer Mathematical Soc2014

KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization

Ilya Loshchilov

This paper investigates the control of an ML component within the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) devoted to black-box optimization. The known CMA-ES weakness is its sample complexity, the number of evaluations of the objective fun ...

2013

Surrogate model

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Self-learning surrogate models in superstructure optimization

Competing Adaptive Networks

Optimization Methods for Control: From Embedded Programmable Hardware to Data-Driven Process Optimization

Aerodynamic shape optimization via surrogate modelling with convolutional neural networks

Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Interpolation Approach

Machine learning methods to assist energy system optimization

On the Use of Transient Information for Static Real-Time Optimization

Integrating Rate Based Models into a Multi-Objective Process Design & Optimisation Framework using Surrogate Models

Analysis of the finite element heterogeneous multiscale method for quasilinear elliptic homogenization problems

KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization

Optimization Methods for Control: From Embedded Programmable Hardware to Data-Driven Process Optimization

Analysis of the finite element heterogeneous multiscale method for quasilinear elliptic homogenization problems

Aerodynamic shape optimization via surrogate modelling with convolutional neural networks

Integrating Rate Based Models into a Multi-Objective Process Design & Optimisation Framework using Surrogate Models

Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Interpolation Approach

Competing Adaptive Networks

Self-learning surrogate models in superstructure optimization

KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization

On the Use of Transient Information for Static Real-Time Optimization

Machine learning methods to assist energy system optimization