4,146 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
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
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
Tools for efficient Deep Learning
In the era of Deep Learning (DL), there is a fast-growing demand for building and deploying Deep Neural Networks (DNNs) on various platforms. This thesis proposes five tools to address the challenges for designing DNNs that are efficient in time, in resources and in power consumption.
We first present Aegis and SPGC to address the challenges in improving the memory efficiency of DL training and inference. Aegis makes mixed precision training (MPT) stabler by layer-wise gradient scaling. Empirical experiments show that Aegis can improve MPT accuracy by at most 4\%. SPGC focuses on structured pruning: replacing standard convolution with group convolution (GConv) to avoid irregular sparsity. SPGC formulates GConv pruning as a channel permutation problem and proposes a novel heuristic polynomial-time algorithm. Common DNNs pruned by SPGC have maximally 1\% higher accuracy than prior work.
This thesis also addresses the challenges lying in the gap between DNN descriptions and executables by Polygeist for software and POLSCA for hardware. Many novel techniques, e.g. statement splitting and memory partitioning, are explored and used to expand polyhedral optimisation. Polygeist can speed up software execution in sequential and parallel by 2.53 and 9.47 times on Polybench/C. POLSCA achieves 1.5 times speedup over hardware designs directly generated from high-level synthesis on Polybench/C.
Moreover, this thesis presents Deacon, a framework that generates FPGA-based DNN accelerators of streaming architectures with advanced pipelining techniques to address the challenges from heterogeneous convolution and residual connections. Deacon provides fine-grained pipelining, graph-level optimisation, and heuristic exploration by graph colouring. Compared with prior designs, Deacon shows resource/power consumption efficiency improvement of 1.2x/3.5x for MobileNets and 1.0x/2.8x for SqueezeNets.
All these tools are open source, some of which have already gained public engagement. We believe they can make efficient deep learning applications easier to build and deploy.Open Acces
Data-driven deep-learning methods for the accelerated simulation of Eulerian fluid dynamics
Deep-learning (DL) methods for the fast inference of the temporal evolution of fluid-dynamics systems, based on the previous recognition of features underlying large sets of fluid-dynamics data, have been studied. Specifically, models based on convolution neural networks (CNNs) and graph neural networks (GNNs) were proposed and discussed.
A U-Net, a popular fully-convolutional architecture, was trained to infer wave dynamics on liquid surfaces surrounded by walls, given as input the system state at previous time-points. A term for penalising the error of the spatial derivatives was added to the loss function, which resulted in a suppression of spurious oscillations and a more accurate location and length of the
predicted wavefronts. This model proved to accurately generalise to complex wall geometries not seen during training.
As opposed to the image data-structures processed by CNNs, graphs offer higher freedom on how data is organised and processed. This motivated the use of graphs to represent the state of fluid-dynamic systems discretised by unstructured sets of nodes, and GNNs to process such graphs. Graphs have enabled more accurate representations of curvilinear geometries and higher resolution placement exclusively in areas where physics is more challenging to resolve. Two novel
GNN architectures were designed for fluid-dynamics inference: the MuS-GNN, a multi-scale GNN, and the REMuS-GNN, a rotation-equivariant multi-scale GNN. Both architectures work by repeatedly passing messages from each node to its nearest nodes in the graph. Additionally, lower-resolutions graphs, with a reduced number of nodes, are defined from the original graph,
and messages are also passed from finer to coarser graphs and vice-versa. The low-resolution graphs allowed for efficiently capturing physics encompassing a range of lengthscales.
Advection and fluid flow, modelled by the incompressible Navier-Stokes equations, were the two types of problems used to assess the proposed GNNs. Whereas a single-scale GNN was sufficient to achieve high generalisation accuracy in advection simulations, flow simulation highly benefited from an increasing number of low-resolution graphs. The generalisation and long-term accuracy of these simulations were further improved by the REMuS-GNN architecture, which
processes the system state independently of the orientation of the coordinate system thanks to a rotation-invariant representation and carefully designed components. To the best of the author’s knowledge, the REMuS-GNN architecture was the first rotation-equivariant and multi-scale GNN.
The simulations were accelerated between one (in a CPU) and three (in a GPU) orders of magnitude with respect to a CPU-based numerical solver. Additionally, the parallelisation of multi-scale GNNs resulted in a close-to-linear speedup with the number of CPU cores or GPUs.Open Acces
DarSIA: An Open-Source Python Toolbox for Two-Scale Image Processing of Dynamics in Porous Media
Understanding porous media flow is inherently a multi-scale challenge, where at the core lies the aggregation of pore-level processes to a continuum, or Darcy-scale, description. This challenge is directly mirrored in image processing, where pore-scale grains and interfaces may be clearly visible in the image, yet continuous Darcy-scale parameters may be what are desirable to quantify. Classical image processing is poorly adapted to this setting, as most techniques do not explicitly utilize the fact that the image contains explicit physical processes. Here, we extend classical image processing concepts to what we define as “physical images” of porous materials and processes within them. This is realized through the development of a new open-source image analysis toolbox specifically adapted to time-series of images of porous materials.publishedVersio
Implementation of a real time Hough transform using FPGA technology
This thesis is concerned with the modelling, design and implementation of efficient architectures for performing the Hough Transform (HT) on mega-pixel resolution real-time images using Field Programmable Gate Array (FPGA) technology. Although the HT has been around for many years and a number of algorithms have been developed it still remains a significant bottleneck in many image processing applications.
Even though, the basic idea of the HT is to locate curves in an image that can be parameterized: e.g. straight lines, polynomials or circles, in a suitable parameter space, the research presented in this thesis will focus only on location of straight lines on binary images. The HT algorithm uses an accumulator array (accumulator bins) to detect the existence of a straight line on an image. As the image needs to be binarized, a novel generic synchronization circuit for windowing operations was designed to perform edge detection. An edge detection method of special interest, the canny method, is used and the design and implementation of it in hardware is achieved in this thesis.
As each image pixel can be implemented independently, parallel processing can be performed. However, the main disadvantage of the HT is the large storage and computational requirements. This thesis presents new and state-of-the-art hardware implementations for the minimization of the computational cost, using the Hybrid-Logarithmic Number System (Hybrid-LNS) for calculating the HT for fixed bit-width architectures. It is shown that using the Hybrid-LNS the computational cost is minimized, while the precision of the HT algorithm is maintained.
Advances in FPGA technology now make it possible to implement functions as the HT in reconfigurable fabrics. Methods for storing large arrays on FPGA’s are presented, where data from a 1024 x 1024 pixel camera at a rate of up to 25 frames per second are processed
Analysing trajectory similarity and improving graph dilation
In this thesis, we focus on two topics in computational geometry. The first topic is analysing trajectory similarity. A trajectory tracks the movement of an object over time. A common way to analyse trajectories is by finding similarities. The Fr\'echet distance is a similarity measure that has gained popularity in the theory community, since it takes the continuity of the curves into account. One way to analyse trajectories using the Fr\'echet distance is to cluster trajectories into groups of similar trajectories. For vehicle trajectories, another way to analyse trajectories is to compute the path on the underlying road network that best represents the trajectory. The second topic is improving graph dilation. Dilation measures the quality of a network in applications such as transportation and communication networks. Spanners are low dilation graphs with not too many edges. Most of the literature on spanners focuses on building the graph from scratch. We instead focus on adding edges to improve the dilation of an existing graph
Fast parametric analysis of trimmed multi-patch isogeometric Kirchhoff-Love shells using a local reduced basis method
This contribution presents a model order reduction framework for real-time
efficient solution of trimmed, multi-patch isogeometric Kirchhoff-Love shells.
In several scenarios, such as design and shape optimization, multiple
simulations need to be performed for a given set of physical or geometrical
parameters. This step can be computationally expensive in particular for real
world, practical applications. We are interested in geometrical parameters and
take advantage of the flexibility of splines in representing complex
geometries. In this case, the operators are geometry-dependent and generally
depend on the parameters in a non-affine way. Moreover, the solutions obtained
from trimmed domains may vary highly with respect to different values of the
parameters. Therefore, we employ a local reduced basis method based on
clustering techniques and the Discrete Empirical Interpolation Method to
construct affine approximations and efficient reduced order models. In
addition, we discuss the application of the reduction strategy to parametric
shape optimization. Finally, we demonstrate the performance of the proposed
framework to parameterized Kirchhoff-Love shells through benchmark tests on
trimmed, multi-patch meshes including a complex geometry. The proposed approach
is accurate and achieves a significant reduction of the online computational
cost in comparison to the standard reduced basis method.Comment: 43 pages, 21 figures, 3 table
Terrain generation algorithms
Procedural terrain generation has become common in games as a whole and in indie games in particular. With procedural terrain generation developers can relatively easily create static or dynamically expanding game areas. Also it is more cost effective since large part of manual work can be automated which traditional game areas would require.
Goal of this thesis is to introduce and evaluate different algorithms that are used or have potential use cases in terrain generation. Such algorithms as various noise functions, which are widely used in the realm of terrain generation, a number of dungeon algorithms, which use variety of methods to generate the dungeon, fractal algorithm, and volumetric terrain generation algorithm which uses a combination of noise and fractal algorithms. Algorithms and techniques will be searched from various scientific articles and literary sources. Metrics used for terrain generation algorithm evaluation will also be introduced, and algorithms in this thesis will be evaluated using these metrics.
During evaluation it was noticed that the evaluated noise functions are generally capable of runtime terrain generation, but are lacking in customization and control since parameters are usually related to the algorithm rather than the resulting terrain. Albeit these shortcomings both Perlin and Simplex noise stand out for their ability to generate good quality terrains. On the other hand most of the evaluated dungeon generation algorithms are incapable of generating terrain during runtime with few exceptions. Also guaranteeing connectivity of rooms or areas in dungeon can be challenge in some algorithms. The introduced fractal algorithm is metrics wise similar to Perlin and Simplex noise even though it uses completely different method to generate the terrain. The volumetric terrain generation algorithm is the only algorithm capable of generating volumetric terrain and its high level of parametrization and customization is its strongest quality
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