Publications related to An Integrated Approach to Designing Robust Gas-Bearing Supported Turbocompressors through Surrogate Modeling and Constrained All-At-Once Multi-Objective Optimization

Related publications (20)

Neuroscience-inspired computer vision and language modeling for behavior analysis

Measuring animal behaviors is a crucial task in a range of scientific applications. Modern approaches with deep neural networks calls for developing solutions that solve a wide range of vision and language tasks. This ubiquitous need for understanding beha ...

EPFL2025

Digital thread in engineering: Concept, state of art, and enabling framework

Dimitrios Kyritsis

The digital thread (DT), a new paradigm for the development of complex engineering systems, has garnered widespread attention in recent years. The DT serves as a key driver and game-changer for the digital transformation of engineering systems due to its a ...

2025

One-Pass Generation of Multivariate Time Series through Conditional Multivariate Modeling

Florent Evariste Forest

In recent years, exploring deep generative models for generating time series has garnered significant interest within the research community. These models have found wide-ranging applications in areas such as data augmentation, scenario simulation, and the ...

Institute of Electrical and Electronics Engineers Inc.2024

Efficient and insightful descriptors for representing molecular and material space

Alexander Jan Goscinski

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

EPFL2024

Automated segmentation and labeling of subcutaneous mouse implants at 14.1T

Gilles Bioley, Dimitri Nestor Alice Van De Ville, Cristina Ramona Cudalbu Moldovan, Maria Giulia Preti, Nicolas Gninenko

Magnetic resonance imaging (MRI) is a valuable tool for studying subcutaneous implants in rodents, providing non-invasive insight into biomaterial conformability and longitudinal characterization. However, considerable variability in existing image analysi ...

FRONTIERS MEDIA SA2023

Communication-efficient distributed training of machine learning models

Thijs Vogels

In this thesis, we explore techniques for addressing the communication bottleneck in data-parallel distributed training of deep learning models. We investigate algorithms that either reduce the size of the messages that are exchanged between workers, or th ...

EPFL2023

Robust optimization of control parameters for WEC arrays using stochastic methods

Tommaso Vanzan

This work presents a new computational optimization framework for the robust control of parks of Wave Energy Converters (WEC) in irregular waves. The power of WEC parks is maximized with respect to the individual control damping and stiffness coefficients ...

Journal of Computational Physics2023

Overflow-free compute memories for edge AI acceleration

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Marco Antonio Rios

Compute memories are memory arrays augmented with dedicated logic to support arithmetic. They support the efficient execution of data-centric computing patterns, such as those characterizing Artificial Intelligence (AI) algorithms. These architectures can ...

2023

Impulsive noise removal via a blind CNN enhanced by an iterative post-processing

Hatef Otroshi Shahreza, Seyedeh Sahar Sadrizadeh

In digital imaging, especially in the process of data acquisition and transmission, images are often affected by impulsive noise. Therefore, it is essential to remove impulsive noise from images before any further processing. Due to the remarkable performa ...

ELSEVIER2022

Distributed adjustable robust optimal power-gas flow considering wind power uncertainty

Colin Neil Jones, Yuning Jiang

The rapid uptake of natural gas-fired units in energy systems poses significant challenges in coordinating the electricity and gas systems. Besides, the uncertainty caused by integrated renewable energy such as wind power raises more requirements on the ro ...

2022

Editorial: Combined water and heat integration in the process industries

François Maréchal

2022

Probing transfer learning with a model of synthetic correlated datasets

Lenka Zdeborová, Luca Saglietti, Federica Gerace

Transfer learning can signiﬁcantly improve the sample eﬃciency of neural networks, by exploiting the relatedness between a data-scarce target task and a data-abundant source task. Despite years of successful applications, transfer learning practice often r ...

2022

Essays in Empirical Asset Pricing

Alexis Arilès Marchal

This thesis consists of three applications of machine learning techniques to empirical asset pricing. In the first part, which is co-authored work with Oksana Bashchenko, we develop a new method that detects jumps nonparametrically in financial time series ...

EPFL2022

A Semisupervised CRF Model for CNN-Based Semantic Segmentation With Sparse Ground Truth

Devis Tuia

Convolutional neural networks (CNNs) represent the new reference approach for semantic segmentation of very-high-resolution (VHR) images, due to their ability to automatically capture semantic information while learning relevant features. However, as for m ...

2022

Quantum Convolutional Circuits For Earth Observation Image Classification

Su Yeon Chang

The amount of study on Quantum Machine Learning (QML) is increasing extensively due to its potential advantages in terms of representational power and computational resources. These advances suggest a possibility to extend its usage into the context of Ear ...

IEEE2022

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Vincent Gramoli

There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs' local computation and training data. Despite its advan ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging

Michaël Unser, Matthias Lütolf, Nathalie Brandenberg, Thanh-An Michel Pham

Quantitative phase imaging (QPI) is an emerging label-free technique that produces images containing morphological and dynamical information without contrast agents. Unfortunately, the phase is wrapped in most imaging system. Phase unwrapping is the comput ...

2021

Defect segmentation for multi-illumination quality control systems

Pascal Fua, Engin Türetken, Siavash Arjomand Bigdeli, David Honzátko

Thanks to recent advancements in image processing and deep learning techniques, visual surface inspection in production lines has become an automated process as long as all the defects are visible in a single or a few images. However, it is often necessary ...

2021

Statistical limits of high-dimensional inference problems

Clément Dominique Luneau

This thesis focuses on two kinds of statistical inference problems in signal processing and data science. The first problem is the estimation of a structured informative tensor from the observation of a noisy tensor in which it is buried. The structure com ...

EPFL2021

From System Level Synthesis to Robust Closed-loop Data-Enabled Predictive Control

Colin Neil Jones, Yingzhao Lian

Willems' fundamental lemma and system level synthesis both characterize a linear dynamic system by its input/output sequences. In this work, we show that the extension of the fundamental lemma in uncertain LTI systems is equivalent to the system level synt ...

IEEE2021