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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...

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...

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...

Images and videos provide a wealth of information for people in production and life. Although most digital information is transmitted via optical fiber, the image acquisition and transmission processes still rely heavily...

Optical neural networks (ONNs) are promising hardware platforms for next-generation neuromorphic computing due to their high parallelism, low latency, and low energy consumption. However, previous integrated photonic ten...

Diffractive Neural Networks (DNNs) leverage the power of light to enhance computational performance in machine learning, offering a pathway to high-speed, low-energy, and large-scale neural information processing. Howeve...

Optical neural networks have significant advantages in terms of power consumption, parallelism, and high computing speed, which has intrigued extensive attention in both academic and engineering communities. It has been ...

Light beams carrying multiple orbital angular momentum (OAM) states, which can be realized by the structured media with phase singularities, have attracted great attentions in the fields of high dimensional optical infor...