Publications associées à A method for aggregating external operating conditions in multi-generation system optimization models

Scalable constrained optimization

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

Robot Learning using Tensor Networks

Suhan Narayana Shetty

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

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

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

On the use of Cramér-Rao Lower Bound for least-variance circuit parameters identification of Li-ion cells

Mario Paolone, Vladimir Sovljanski

Electrochemical Impedance Spectroscopy (EIS) and Equivalent Circuit Models (ECMs) are widely used to characterize the impedance and estimate parameters of electrochemical systems such as batteries. We use a generic ECM with ten parameters grouped to model ...

2024

The effect of smooth parametrizations on nonconvex optimization landscapes

Nicolas Boumal

We develop new tools to study landscapes in nonconvex optimization. Given one optimization problem, we pair it with another by smoothly parametrizing the domain. This is either for practical purposes (e.g., to use smooth optimization algorithms with good g ...

Springer Heidelberg2024

Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization

Patrick Thiran

Bayesian Optimization (BO) is typically used to optimize an unknown function f that is noisy and costly to evaluate, by exploiting an acquisition function that must be maximized at each optimization step. Even if provably asymptotically optimal BO algorith ...

2024

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

Colin Neil Jones, Wenjie Xu, Bratislav Svetozarevic

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

BENIGN LANDSCAPES OF LOW-DIMENSIONAL RELAXATIONS FOR ORTHOGONAL SYNCHRONIZATION ON GENERAL GRAPHS

Nicolas Boumal

Orthogonal group synchronization is the problem of estimating n elements Z(1),& mldr;,Z(n) from the rxr orthogonal group given some relative measurements R-ij approximate to Z(i)Z(j)(-1). The least-squares formulation is nonconvex. To avoid its local minim ...

Siam Publications2024

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

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

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

Contemporary Logic Synthesis: with an Application to AQFP Circuit Optimization

Siang-Yun Lee

Electronic devices play an irreplaceable role in our lives. With the tightening time to market, exploding demand for computing power, and continuous desire for smaller, faster, less energy-consuming, and lower-cost chips, computer-aided design for electron ...

EPFL2024

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

The complexity of quantum support vector machines

Stefan Woerner, Gian Florin Gentinetta

Quantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any known classical algorithm for certain data sets. The training of such models c ...

Verein Forderung Open Access Publizierens Quantenwissenschaf2024

Experimental characterization and stochastic models for time-dependent rupture of thin-ply composite laminates

Thin-laminate composites with thicknesses below 200 mu m hold significant promise for future, larger, and lighter deployable structures. This paper presents a study of the time-dependent failure behavior of thin carbon-fiber laminates under bending, focusi ...

2024

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Julian Thomas Blackwell, Tanja Christina Käser Jacober, Paola Mejia Domenzain, Vinitra Swamy, Isadora Alves de Salles

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

2024

Robust optimization of control parameters for WEC arrays using stochastic methods

Edie Miglio, 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

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

Sayan Ghosh, Chao Hu, 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

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

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

François Maréchal, Yuxuan Li

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