Publications associées à Shape constrained splines as transparent black-box models for bioprocess modeling

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...

2024

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Hugo Chao Cui, Florent Gérard Krzakala, Lenka Zdeborová

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

Closed-loop data-driven modeling and distributed control for islanded microgrids with input constraints

Alireza Karimi, Seyed Sohail Madani, Dongdong Zheng

In this paper a new nonlinear identification method for microgrids based on neural networks is proposed. The system identification process can be done using the available closed-loop system input/output data recorded during normal operation without additio ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

Sundar Subramaniam Ganesh

This work aims to study the effects of wind uncertainties in civil engineering structural design. Optimising the design of a structure for safety or operability without factoring in these uncertainties can result in a design that is not robust to these per ...

EPFL2022

Control-oriented modeling and analysis of tubular dielectric elastomer actuators dedicated to cardiac assist devices

Yves Perriard, Yoan René Cyrille Civet, Ning Liu, Thomas Guillaume Martinez, Armando Matthieu Walter

This paper deals with the control-oriented modeling of a multilayered dielectric elastomer actuator based tube. The actuator is clamped at both sides and performs a radial displacement. The hyperelastic deformation and the viscoelastic performance, togethe ...

2022

Model order reduction based on functional rational approximants for parametric PDEs with meromorphic structure

Davide Pradovera

Many engineering fields rely on frequency-domain dynamical systems for the mathematical modeling of physical (electrical/mechanical/etc.) structures. With the growing need for more accurate and reliable results, the computational burden incurred by frequen ...

EPFL2021

Linear regression analysis of regional mean speed of Athens city network using drone data: A multi-modal approach

Nikolaos Geroliminis, Emmanouil Barmpounakis

The work proposes a multi-modal regional mean speed regression analysis for the city network of Athens, Greece. The dataset from pNUEMA experiment is used in the present context. Accumulations and mean speeds of different modes are estimated and compared t ...

2021

Physics-informed machine learning for reduced-order modeling of nonlinear problems

Qian Wang, Jan Sickmann Hesthaven

A reduced basis method based on a physics-informed machine learning framework is developed for efficient reduced-order modeling of parametrized partial differential equations (PDEs). A feedforward neural network is used to approximate the mapping from the ...

2021

Travel Time Prediction for Congested Freeways With a Dynamic Linear Model

Nikolaos Geroliminis, Semin Kwak

Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction a challenging task. Here we propose to use dynamic linear models (DLMs) to a ...

IEEE Institute of Electrical and Electronics Engineers2021

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

Jan Sickmann Hesthaven, Mariella Kast, Mengwu Guo

We propose a non-intrusive reduced basis (RB) method for parametrized nonlinear partial differential equations (PDEs) that leverages models of different accuracy. From a collection of low-fidelity (LF) snapshots, parameter locations are extracted for the e ...

ELSEVIER SCIENCE SA2020

Heteronuclear soliton molecules in optical microresonators

Tobias Kippenberg, Tobias Herr, Erwan Guillaume Albert Lucas, Wenle Weng

Optical soliton molecules are bound states of solitons that arise from the balance between attractive and repulsive effects. Having been observed in systems ranging from optical fibres to mode-locked lasers, they provide insights into the fundamental inter ...

Nature Research2020

A note on efficient computation of privileged directions in modifier adaptation

Dominique Bonvin, Alejandro Gabriel Marchetti, Martand Singhal, Timm Faulwasser

In the presence of plant-model mismatch, the estimation of plant gradients is key to the performance of measurement-based iterative optimization schemes. However, gradient estimation requires time-consuming experiments, wherein the plant is sequentially pe ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations

Michel Bierlaire, Prateek Bansal

Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models. It has been established that VB is substan ...

PERGAMON-ELSEVIER SCIENCE LTD2020

A Global-Local Approach For Detecting Hotspots In Multiple-Response Regression

Anthony Christopher Davison, Hélène Ruffieux, Jörg Hager

We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, that is, predictors associated with several responses. Such a task is critical in statistical genetics, ...

INST MATHEMATICAL STATISTICS2020

An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation

Volkan Cevher, Jonathan Mark Scarlett

Information theory plays an indispensable role in the development of algorithm-independent impossibility results, both for communication problems and for seemingly distinct areas such as statistics and machine learning. While numerous information-theoretic ...

Cambridge University Press2021

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning

Peyman Mohajerin Esfahani, Viet Anh Nguyen, Daniel Kuhn, Soroosh Shafieezadeh Abadeh

Many decision problems in science, engineering and economics are affected by uncertain parameters whose distribution is only indirectly observable through samples. The goal of data-driven decision-making is to learn a decision from finitely many training s ...

2019

Automatic L2 Regularization for Multiple Generalized Additive Models

Yousra El Bachir

Multiple generalized additive models are a class of statistical regression models wherein parameters of probability distributions incorporate information through additive smooth functions of predictors. The functions are represented by basis function expan ...

EPFL2019

Electron density learning of non-covalent systems

Anne-Clémence Corminboeuf, Alberto Fabrizio, Michele Ceriotti, Andrea Grisafi, Benjamin André René Meyer

Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origi ...

2019

Real-Time Optimization of Interconnected Systems via Modifier Adaptation, with Application to Gas-Compressor Stations

Predrag Milosavljevic

The process industries are characterized by a large number of continuously operating plants, for which optimal operation is of economic and ecological importance. Many industrial systems can be regarded as an arrangement of several subsystems, where outp ...

EPFL2018

Concept of Variants and Invariants for Reaction Systems, with Application to Estimation, Control and Optimization

Diogo Filipe Mateus Rodrigues

The concept of reaction variants and invariants for lumped reaction systems has been known for several decades. Its applications encompass model identification, data reconciliation, state estimation and control using kinetic models. In this thesis, the con ...

EPFL2018