4,080 research outputs found
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
The State of the Art in Deep Learning Applications, Challenges, and Future Prospects::A Comprehensive Review of Flood Forecasting and Management
Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure
Bayesian Forecasting in Economics and Finance: A Modern Review
The Bayesian statistical paradigm provides a principled and coherent approach
to probabilistic forecasting. Uncertainty about all unknowns that characterize
any forecasting problem -- model, parameters, latent states -- is able to be
quantified explicitly, and factored into the forecast distribution via the
process of integration or averaging. Allied with the elegance of the method,
Bayesian forecasting is now underpinned by the burgeoning field of Bayesian
computation, which enables Bayesian forecasts to be produced for virtually any
problem, no matter how large, or complex. The current state of play in Bayesian
forecasting in economics and finance is the subject of this review. The aim is
to provide the reader with an overview of modern approaches to the field, set
in some historical context; and with sufficient computational detail given to
assist the reader with implementation.Comment: The paper is now published online at:
https://doi.org/10.1016/j.ijforecast.2023.05.00
Soundscape in Urban Forests
This Special Issue of Forests explores the role of soundscapes in urban forested areas. It is comprised of 11 papers involving soundscape studies conducted in urban forests from Asia and Africa. This collection contains six research fields: (1) the ecological patterns and processes of forest soundscapes; (2) the boundary effects and perceptual topology; (3) natural soundscapes and human health; (4) the experience of multi-sensory interactions; (5) environmental behavior and cognitive disposition; and (6) soundscape resource management in forests
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
Quadratic quantum speedup in evaluating bilinear risk functions
Computing nonlinear functions over multilinear forms is a general problem
with applications in risk analysis. For instance in the domain of energy
economics, accurate and timely risk management demands for efficient simulation
of millions of scenarios, largely benefiting from computational speedups. We
develop a novel hybrid quantum-classical algorithm based on polynomial
approximation of nonlinear functions and compare different implementation
variants. We prove a quadratic quantum speedup, up to polylogarithmic factors,
when forms are bilinear and approximating polynomials have second degree, if
efficient loading unitaries are available for the input data sets. We also
enhance the bidirectional encoding, that allows tuning the balance between
circuit depth and width, proposing an improved version that can be exploited
for the calculation of inner products. Lastly, we exploit the dynamic circuit
capabilities, recently introduced on IBM Quantum devices, to reduce the average
depth of the Quantum Hadamard Product circuit. A proof of principle is
implemented and validated on IBM Quantum systems
Analog Photonics Computing for Information Processing, Inference and Optimisation
This review presents an overview of the current state-of-the-art in photonics
computing, which leverages photons, photons coupled with matter, and
optics-related technologies for effective and efficient computational purposes.
It covers the history and development of photonics computing and modern
analogue computing platforms and architectures, focusing on optimization tasks
and neural network implementations. The authors examine special-purpose
optimizers, mathematical descriptions of photonics optimizers, and their
various interconnections. Disparate applications are discussed, including
direct encoding, logistics, finance, phase retrieval, machine learning, neural
networks, probabilistic graphical models, and image processing, among many
others. The main directions of technological advancement and associated
challenges in photonics computing are explored, along with an assessment of its
efficiency. Finally, the paper discusses prospects and the field of optical
quantum computing, providing insights into the potential applications of this
technology.Comment: Invited submission by Journal of Advanced Quantum Technologies;
accepted version 5/06/202
Turbulence closure with small, local neural networks: Forced two-dimensional and -plane flows
We parameterize sub-grid scale (SGS) fluxes in sinusoidally forced
two-dimensional turbulence on the -plane at high Reynolds numbers
(Re25000) using simple 2-layer Convolutional Neural Networks (CNN) having
only O(1000)parameters, two orders of magnitude smaller than recent studies
employing deeper CNNs with 8-10 layers; we obtain stable, accurate, and
long-term online or a posteriori solutions at 16X downscaling factors. Our
methodology significantly improves training efficiency and speed of online
Large Eddy Simulations (LES) runs, while offering insights into the physics of
closure in such turbulent flows. Our approach benefits from extensive
hyperparameter searching in learning rate and weight decay coefficient space,
as well as the use of cyclical learning rate annealing, which leads to more
robust and accurate online solutions compared to fixed learning rates. Our CNNs
use either the coarse velocity or the vorticity and strain fields as inputs,
and output the two components of the deviatoric stress tensor. We minimize a
loss between the SGS vorticity flux divergence (computed from the
high-resolution solver) and that obtained from the CNN-modeled deviatoric
stress tensor, without requiring energy or enstrophy preserving constraints.
The success of shallow CNNs in accurately parameterizing this class of
turbulent flows implies that the SGS stresses have a weak non-local dependence
on coarse fields; it also aligns with our physical conception that small-scales
are locally controlled by larger scales such as vortices and their strained
filaments. Furthermore, 2-layer CNN-parameterizations are more likely to be
interpretable and generalizable because of their intrinsic low dimensionality.Comment: 27 pages, 13 figure
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