Concept

Grey box model

Related publications (11)

Towards Trustworthy Deep Learning for Image Reconstruction

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

EPFL2024

3D Common Corruptions and Data Augmentation

Amir Roshan Zamir, Oguzhan Fatih Kar, Andrei Atanov, Shuqing Teresa Yeo

We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models as well as data augmentation mechanisms for training neural networks. The primary distinction of the proposed transformations is that, unlike e ...

IEEE COMPUTER SOC2022

Introducing reinforcement learning to the energy system design process

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

Design optimization of distributed energy systems has become an interest of a wider group of researchers due the capability of these systems to integrate non-dispatchable renewable energy technologies such as solar PV and wind. White box models, using line ...

2020

Modeling and Assessment of Urban Energy Systems

Amarasinghage Tharindu Dasun Perera

Improving the energy sustainability of our cities involves the integration of multiple renewable energy technologies into existing energy infrastructure, stretching the capabilities of traditional energy systems to the limit. To consider the transition tak ...

EPFL2019

An Analytical Model of Hardware Transactional Memory

Willy Zwaenepoel, Paolo Romano, Diego Didona

This paper investigates the problem of deriving a white box performance model of Hardware Transactional Memory (HTM) systems. The proposed model targets TSX, a popular implementation of HTM integrated in Intel processors starting with the Haswell family in ...

2017

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

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

Online optimisation and detection of process upset in semi-batch reactors using a kinetic modelling approach

Julien Léo Billeter, Konrad Hungerbühler

Process Analytical Chemistry/Technology has tremendously evolved in the last decades due to the development of multivariate online sensors that are able to monitor the properties of industrial processes in real time [1, 2]. Online monitoring of product qua ...

2008

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