Zobrazeno 1 - 10
of 5 846
pro vyhledávání: '"Magrì, A."'
Autor:
Myers, Samuel A., Howell, Ellen S., Magri, Christopher, Vervack Jr., Ronald J., Fernández, Yanga R., Hinkle, Mary L., Marshall, Sean E.
Publikováno v:
Planet. Sci. J. 5 238 (2024)
Understanding the properties of near-Earth asteroids (NEAs) is key for many aspects of planetary science, particularly planetary defense. Our current knowledge of NEA sizes and regolith properties is heavily dependent on simple thermal models. These
Externí odkaz:
http://arxiv.org/abs/2410.24101
Autor:
Özalp, Elise, Magri, Luca
The data-driven learning of solutions of partial differential equations can be based on a divide-and-conquer strategy. First, the high dimensional data is compressed to a latent space with an autoencoder; and, second, the temporal dynamics are inferr
Externí odkaz:
http://arxiv.org/abs/2410.18003
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
Precision spraying evaluation requires automation primarily in post-spraying imagery. In this paper we propose an eXplainable Artificial Intelligence (XAI) computer vision pipeline to evaluate a precision spraying system post-spraying without the nee
Externí odkaz:
http://arxiv.org/abs/2409.16213
In this paper, we propose hardware and software enhancements for the Pepper robot to improve its human-robot interaction capabilities. This includes the integration of an NVIDIA Jetson GPU to enhance computational capabilities and execute real time a
Externí odkaz:
http://arxiv.org/abs/2409.01036
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