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La mayoría de los modelos matemáticos empleados para describir fenómenos físicos reales en ciencia e ingeniería están gobernados por ecuaciones parciales diferenciales no-lineales dependientes del tiempo PDEs (Part...

Wave-based signal processing has witnessed a significant expansion of interest in a variety of science and engineering disciplines, as it provides new opportunities for achieving high-speed and low-power operations. Alth...

Photonic reservoir computing has been intensively investigated to solve machine learning tasks effectively. A simple learning procedure of output weights is used for reservoir computing. However, the lack of training of ...

Exploiting alternative physical dimensions beyond the spatial domain has been intensively explored to improve the scalability in photonic computing. One approach leverages dynamical systems for time-domain computation, e...

Accurately forecasting the high-dimensional chaotic dynamics of semiconductor laser (SL) networks is essential in photonics research. In this study, we propose a spatiotemporal multiplexed photonic reservoir computing (S...

This thesis, entitled €High Performance Computing for solving large sparse systems. Optical Diffraction Tomography as a case of study€ investigates the computational issues related to the resolution of linear systems o...

Efficiently solving the user equilibrium traffic assignment problem with elastic demand (UE-TAPED) for transportation networks is a critical problem for transportation studies. Most existing UE-TAPED algorithms are desig...

In previous works, we proposed two methods for data clustering based on automatically discovered fuzzy predicates which were referred to as SOM-based Fuzzy Predicate Clustering (SFPC) [Meschino et al., Neurocomputing, 14...

Abstract Long-range dependency processing is the core challenge of sequence modeling, and traditional sequence modeling algorithms suffer from information decay and low parallel computing efficiency in ultra long sequenc...

This paper presents an innovative parallel computational methodology employing adaptive fractional differential operators to address Monkeypox epidemiological modeling challenges. The complex dynamics of such models ofte...

An existing hybrid MPI-OpenMP scheme is augmented with a CUDA-based fine grain parallelization approach for multidimensional distributed Fourier transforms, in a well-characterized pseudospectral fluid turbulence code. B...

Photonic reservoir computing (RC) is a simple and efficient neuromorphic computing framework for human cortical circuits, which is featured with fast training speed and low training cost. Photonic time delay RC, as a sim...

Integrated photonic devices and artificial intelligence have presented a significant opportunity for the advancement of optical computing in practical applications. Optical computing technology is a unique computing syst...

The simultaneous advances in artificial neural networks and photonic integration technologies have spurred extensive research in optical computing and optical neural networks (ONNs). The potential to simultaneously explo...

Abstract The objective of this paper is to develop a parallel Pole numerical integration method suitable for renaming in MIMD computing systems. In this paper we improve the Pole Method for numerical integration by parti...

While conventional von Neumann based machines are increasingly challenged by modern day requirements, electromagnetic analog computing devices promise to provide a platform that is highly parallel, efficient and fast. Al...

Optical neural networks (ONNs) have demonstrated unique advantages in overcoming the limitations of traditional electronic computing through their inherent physical properties, including high parallelism, ultra-wide band...

The integration of artificial intelligence (AI) systems in the daily life greatly increases the amount of data generated and processed. In addition to the large computational power required, the hardware needs to be comp...

State-of-the-art deep learning models can converse and interact with humans by understanding their emotions, but the exponential increase in model parameters has triggered an unprecedented demand for fast and low-power c...

When solving, modeling or reasoning about complex problems, it is usually convenient to use the knowledge of a parallel physical system for representing it. This is the case of lumped-circuit abstraction, which can be us...

Ising machines are novel computing devices for the energy minimization of Ising models. These combinatorial optimization problems are of paramount importance for science and technology, but remain difficult to tackle on ...

Analog optical computing has been an innovation and research interest in last several years, thanks to the ultra-high speed (potential for real-time processing), ultra-low power consumption and capability of parallel pro...

Optical computing has gained significant attention as a potential solution to the growing computational demands of machine learning, particularly for tasks requiring large-scale data processing and high energy efficiency...

Optical computing offers potential for ultra high-speed and low-latency computation by leveraging the intrinsic properties of light, such as parallelism and linear as well as nonlinear ultra-high bandwidth signal transfo...