Concept
Optimisation par essaims particulaires
Publications associées (296)
Contemporary Logic Synthesis: with an Application to AQFP Circuit Optimization
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 ...
EPFL2024Violation-aware contextual Bayesian optimization for controller performance optimization with unmodeled constraints
Colin Neil Jones, Bratislav Svetozarevic, 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 Ltd2024Distributed Predictive Formation Control of Autonomous Rotary-Wing Micro Aerial Vehicles
This doctoral thesis navigates the complex landscape of motion coordination and formation control within teams of rotary-wing Micro Aerial Vehicles (MAVs). Prompted by the intricate demands of real-world applications such as search and rescue or surveillan ...
EPFL2024Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes
Silvestro Micera, Simone Romeni, Elena Losanno, Luca Pierantoni
Objective. Peripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon ...
IOP Publishing Ltd2023Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search
Rachid Guerraoui, Andrei Kucharavy, Matteo Monti
Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...
New York2023Universal and adaptive methods for robust stochastic optimization
Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...
EPFL2023A comprehensive review of digital twin-part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives
Olga Fink, Chao Hu, Sayan Ghosh
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 ...
SPRINGER2023Robust 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 ...
2023Robust 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 ...
2023RMAML: Riemannian meta-learning with orthogonality constraints
Meta-learning is the core capability that enables intelligent systems to rapidly generalize their prior ex-perience to learn new tasks. In general, the optimization-based methods formalize the meta-learning as a bi-level optimization problem, that is a nes ...
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