Zobrazeno 1 - 10
of 276
pro vyhledávání: '"Magri, Luca"'
Autor:
Özalp, Elise, Magri, Luca
Partial differential equations, and their chaotic solutions, are pervasive in the modelling of complex systems in engineering, science, and beyond. Data-driven methods can find solutions to partial differential equations with a divide-and-conquer str
Externí odkaz:
http://arxiv.org/abs/2410.00480
Optimal training of finitely-sampled quantum reservoir computers for forecasting of chaotic dynamics
In the current Noisy Intermediate Scale Quantum (NISQ) era, the presence of noise deteriorates the performance of quantum computing algorithms. Quantum Reservoir Computing (QRC) is a type of Quantum Machine Learning algorithm, which, however, can ben
Externí odkaz:
http://arxiv.org/abs/2409.01394
Autor:
Mo, Yaxin, Magri, Luca
Data from fluid flow measurements are typically sparse, noisy, and heterogeneous, often from mixed pressure and velocity measurements, resulting in incomplete datasets. In this paper, we develop a physics-constrained convolutional neural network, whi
Externí odkaz:
http://arxiv.org/abs/2409.00260
The identification of self-similarity is an indispensable tool for understanding and modelling physical phenomena. Unfortunately, this is not always possible to perform formally in highly complex problems. We propose a methodology to extract the simi
Externí odkaz:
http://arxiv.org/abs/2407.10724
A large spectrum of problems in classical physics and engineering, such as turbulence, is governed by nonlinear differential equations, which typically require high-performance computing to be solved. Over the past decade, however, the growth of clas
Externí odkaz:
http://arxiv.org/abs/2406.04826
Deep Learning (DL) models have been successfully applied to many applications including biomedical cell segmentation and classification in histological images. These models require large amounts of annotated data which might not always be available,
Externí odkaz:
http://arxiv.org/abs/2406.01403
We introduce a new family of minimal problems for reconstruction from multiple views. Our primary focus is a novel approach to autocalibration, a long-standing problem in computer vision. Traditional approaches to this problem, such as those based on
Externí odkaz:
http://arxiv.org/abs/2405.05605
In chaotic dynamical systems, extreme events manifest in time series as unpredictable large-amplitude peaks. Although deterministic, extreme events appear seemingly randomly, which makes their forecasting difficult. By learning the dynamics from obse
Externí odkaz:
http://arxiv.org/abs/2405.03390
When they occur, azimuthal thermoacoustic oscillations can detrimentally affect the safe operation of gas turbines and aeroengines. We develop a real-time digital twin of azimuthal thermoacoustics of a hydrogen-based annular combustor. The digital tw
Externí odkaz:
http://arxiv.org/abs/2404.18793
Turbulent flows are chaotic and multi-scale dynamical systems, which have large numbers of degrees of freedom. Turbulent flows, however, can be modelled with a smaller number of degrees of freedom when using the appropriate coordinate system, which i
Externí odkaz:
http://arxiv.org/abs/2404.19660