Publications associées à Weight and efficiency optimization strategy of an interleaved DC-DC converter for a solar aircraft

Publications associées (31)

Robot Learning using Tensor Networks

In various robotics applications, the selection of function approximation methods greatly influences the feasibility and computational efficiency of algorithms. Tensor Networks (TNs), also referred to as tensor decomposition techniques, present a versatile ...

EPFL2024

Scalable constrained optimization

Maria-Luiza Vladarean

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

EPFL2024

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

Silvestro Micera, Simone Romeni

A method for optimizing at least one of a geometry, an implantation procedure, and/or stimulation protocol of one or more electrodes for an electrical stimulation of a target structure in a nervous system of a living being by a computer device, the method ...

2024

Violation-aware contextual Bayesian optimization for controller performance optimization with unmodeled constraints

Colin Neil Jones, Wenjie Xu

We study the problem of performance optimization of closed -loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for improving closed -loop performance by automatically tuning controller gains or re ...

Elsevier Sci Ltd2024

Electrochemical devices and computer science: water/thermal management of proton exchange membrane fuel cells and electrolyzers in different scales

Hossein Pourrahmani

The current restrictions on the registration of combustion engines in different countries and the harmful impacts of fossil fuels on the environment and human health have motivated decision-makers to use batteries and/or fuel cells as alternatives for comb ...

EPFL2023

Biophysically accurate and machine learning-based surrogate models to optimize neuroprosthesis design and operation

Simone Romeni

Electrical stimulation of the nervous system has emerged as a promising assistive technology in case of many injuries and illnesses across various parts of the nervous system. In particular, the invasive neuromodulation of the peripheral nervous system see ...

EPFL2024

Mesh-based topology, shape and sizing optimization of ribbed plates

Ahmad Abdulrahman Ahmad Majdouba

In this paper, we present a new parameterization and optimization procedure for minimizing the weight of ribbed plates. The primary goal is to reduce embodied CO2 in concrete floors as part of the effort to diminish the carbon footprint of the construction ...

Springer2024

Stochastic-robust planning optimization method based on tracking-economy extreme scenario tradeoff for CCHP multi-energy system

François Maréchal

The load tracking performance of combined cooling, heating, and power multi-energy system (CCHP-MES) greatly depends on the equipment capacity configuration. And the frequent fluctuations in the source-load uncertainty puts higher demands on the load track ...

PERGAMON-ELSEVIER SCIENCE LTD2023

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering

François Maréchal, Julia Granacher

In Process Systems Engineering, computationally-demanding models are frequent and plentiful. Handling such complexity in an optimization framework in a fast and reliable way is essential, not only for generating meaningful solutions but also for providing ...

Pergamon-Elsevier Science Ltd2023

Robust Design of Herringbone Grooved Journal Bearings using Multi-Objective Optimization with Artificial Neural Networks

Jürg Alexander Schiffmann, Soheyl Massoudi

Herringbone grooved journal bearings (HGJBs) are widely used in micro-turbocompressor applications due to their high load-carrying capacity, low friction, and oil-free solution. However, the performance of these bearings is sensitive to manufacturing devia ...

Amer Soc Mechanical Engineers2023

Modeling and Design Optimization of Loosely Coupled PCB Spiral Coils in Inductive Power Transfer Systems

Drazen Dujic, Xiaotong Du

There are various possibilities to realize coil winding designs for an inductive power transfer system. In order to achieve high power transfer efficiency and power density and explore trade-offs between the two, design optimization around the coil link is ...

2023

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

Electrification and digitalization effects on sectoral energy demand and consumption: A prospective study towards 2050

François Maréchal, Xiang Li, Dorsan Alexis A. Lepour, Fabian Heymann

Energy transition is blurring the boundaries between the demand and supply sides with growing participation of prosumer resources. The intensifying pace of electrification and digitalization during the past decade tends to continue, leading to potential te ...

PERGAMON-ELSEVIER SCIENCE LTD2023

C-shells: Deployable Gridshells with Curved Beams

Mark Pauly, Francis Julian Panetta, Yingying Ren, Quentin Christian Becker, Seiichi Eduardo Suzuki Erazo, Davide Pellis

We introduce a computational pipeline for simulating and designing C-shells, a new class of planar-to-spatial deployable linkage structures. A C-shell is composed of curved flexible beams connected at rotational joints that can be assembled in a stress-fre ...

Association for Computing Machinery (ACM)2023

A comprehensive review of digital twin-part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives

Olga Fink

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision- and policy-making, and more, by comprehensively m ...

SPRINGER2023

Bayesian Optimization for Chemical Reactions

Philippe Schwaller, Jeff Guo, Bojana Rankovic

Reaction optimization is challenging and traditionally delegated to domain experts who iteratively pro-pose increasingly optimal experiments. Problematically, the reaction landscape is complex and often requires hundreds of experiments to reach convergence ...

Swiss Chemical Soc2023

Hardware-Software co-design Methodologies for Edge AI Optimization

Flavio Ponzina

ML-based edge devices may face memory and computational errors that affect applications' reliability and performance. These errors can be the result of particular working conditions (e.g., radiation areas in physical experiments or avionics) or could be th ...

EPFL2023

Data-driven Methods for Control: from Linear to Lifting

Yingzhao Lian

The progress towards intelligent systems and digitalization relies heavily on the use of automation technology. However, the growing diversity of control objects presents significant challenges for traditional control approaches, as they are highly depende ...

EPFL2023

ROSMOSE: A Web-based Optimization Tool to Aid Decision-making for the Design and Operation of Industrial and Urban Energy Systems

François Maréchal, Daniel Alexander Florez Orrego, Meire Ellen Gorete Ribeiro Domingos, Cédric Terrier, Michel Lopez

Energy efficiency is crucial for the sustainable operation of all industrial and urban sectors. However, practicing engineers have seldom access to open-source tools that can readily evaluate and compare scenarios in terms of energy consumption, cost, and ...

2023

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

Christophe Ballif, Nicolas Würsch, Laure-Emmanuelle Perret Aebi, Marine Dominique Cauz

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...

2023