相关论文: Free-Space Optical Channel Turbulence Prediction: …
Failure management plays a significant role in optical networks. It ensures secure operation, mitigates potential risks, and executes proactive protection. Machine learning (ML) is considered to be an extremely powerful technique for…
The free-space optical channel has the potential to facilitate inexpensive, wireless communication with fiber-like bandwidth under short deployment timelines. However, atmospheric effects can significantly degrade the reliability of a…
In this work, we present a unified performance analysis of a free-space optical (FSO) link that accounts for pointing errors and both types of detection techniques (i.e. intensity modulation/direct detection (IM/DD) as well as heterodyne…
The free-space optical (FSO) communication links with orbital angular momentum (OAM) multiplexing have been demonstrated that they can largely enhance the systems' capacity without a corresponding increase in spectral bandwidth, but the…
This work presents a review of the current state of research in data-driven turbulence closure modeling. It offers a perspective on the challenges and open issues, but also on the advantages and promises of machine learning methods applied…
We propose a general model-free strategy for feedback control design of turbulent flows. This strategy called 'machine learning control' (MLC) is capable of exploiting nonlinear mechanisms in a systematic unsupervised manner. It relies on…
In this paper, we investigate video signal transmission through an FPGA-based free-space optical (FSO) communication system prototype. We use a channel emulator that models the turbulence, scintillation, and power attenuation of the FSO…
Spectrum monitoring and interference detection are crucial for the satellite service performance and the revenue of SatCom operators. Interference is one of the major causes of service degradation and deficient operational efficiency.…
Advancing our understanding of astrophysical turbulence is bottlenecked by the limited resolution of numerical simulations that may not fully sample scales in the inertial range. Machine learning (ML) techniques have demonstrated promise in…
Predicting the outcome of liquid droplet collisions is an extensively studied phenomenon but the current physics based models for predicting the outcomes are poor (accuracy $\approx 43\%$). The key weakness of these models is their limited…
Meta-Learning (ML) has proven to be a useful tool for training Few-Shot Learning (FSL) algorithms by exposure to batches of tasks sampled from a meta-dataset. However, the standard training procedure overlooks the dynamic nature of the…
With the recent rapid developments in machine learning (ML), several attempts have been made to apply ML methods to various fluid dynamics problems. However, the feasibility of ML for predicting turbulence dynamics has not yet been explored…
Flow-level simulation is widely used to model large-scale data center networks due to its scalability. Unlike packet-level simulators that model individual packets, flow-level simulators abstract traffic as continuous flows with dynamically…
While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this paper, we aim…
Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc.…
A fully-convolutional neural-network model is used to predict the streamwise velocity fields at several wall-normal locations by taking as input the streamwise and spanwise wall-shear-stress planes in a turbulent open channel flow. The…
Accurately predicting turbulent flows remains a central challenge in fluid dynamics due to their high dimensionality and intrinsic nonlinearity. Recent developments in quantum algorithms and machine learning offer new opportunities for…
The practical deployment of high-capacity free-space optical communication is fundamentally limited by atmospheric turbulence, a challenge that conventional mode-sorting techniques have failed to overcome. While engineered optical computers…
Atmospheric attenuation, atmospheric turbulence, geometric spread, and pointing errors, degrade the performance of free-space optical transmission. In the weak turbulence regime, the probability density function describing the distribution…
Turbulent flows play an important role in many scientific and technological design problems. Both Sub-Grid Scale (SGS) models in Large Eddy Simulations (LES) and Reynolds Averaged Navier Stokes (RANS) based modeling will require turbulence…