7,614 research outputs found

    Quantum algorithm for smoothed particle hydrodynamics

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    We present a quantum computing algorithm for the smoothed particle hydrodynamics (SPH) method. We use a normalization procedure to encode the SPH operators and domain discretization in a quantum register. We then perform the SPH summation via an inner product of quantum registers. Using a one-dimensional function, we test the approach in a classical sense for the kernel sum and first and second derivatives of a one-dimensional function, using both the Gaussian and Wendland kernel functions, and compare various register sizes against analytical results. Error convergence is exponentially fast in the number of qubits. We extend the method to solve the one-dimensional advection and diffusion partial differential equations, which are commonly encountered in fluids simulations. This work provides a foundation for a more general SPH algorithm, eventually leading to highly efficient simulations of complex engineering problems on gate-based quantum computers

    Classical and quantum algorithms for scaling problems

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    This thesis is concerned with scaling problems, which have a plethora of connections to different areas of mathematics, physics and computer science. Although many structural aspects of these problems are understood by now, we only know how to solve them efficiently in special cases.We give new algorithms for non-commutative scaling problems with complexity guarantees that match the prior state of the art. To this end, we extend the well-known (self-concordance based) interior-point method (IPM) framework to Riemannian manifolds, motivated by its success in the commutative setting. Moreover, the IPM framework does not obviously suffer from the same obstructions to efficiency as previous methods. It also yields the first high-precision algorithms for other natural geometric problems in non-positive curvature.For the (commutative) problems of matrix scaling and balancing, we show that quantum algorithms can outperform the (already very efficient) state-of-the-art classical algorithms. Their time complexity can be sublinear in the input size; in certain parameter regimes they are also optimal, whereas in others we show no quantum speedup over the classical methods is possible. Along the way, we provide improvements over the long-standing state of the art for searching for all marked elements in a list, and computing the sum of a list of numbers.We identify a new application in the context of tensor networks for quantum many-body physics. We define a computable canonical form for uniform projected entangled pair states (as the solution to a scaling problem), circumventing previously known undecidability results. We also show, by characterizing the invariant polynomials, that the canonical form is determined by evaluating the tensor network contractions on networks of bounded size

    Towards Neuromorphic Gradient Descent: Exact Gradients and Low-Variance Online Estimates for Spiking Neural Networks

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    Spiking Neural Networks (SNNs) are biologically-plausible models that can run on low-powered non-Von Neumann neuromorphic hardware, positioning them as promising alternatives to conventional Deep Neural Networks (DNNs) for energy-efficient edge computing and robotics. Over the past few years, the Gradient Descent (GD) and Error Backpropagation (BP) algorithms used in DNNs have inspired various training methods for SNNs. However, the non-local and the reverse nature of BP, combined with the inherent non-differentiability of spikes, represent fundamental obstacles to computing gradients with SNNs directly on neuromorphic hardware. Therefore, novel approaches are required to overcome the limitations of GD and BP and enable online gradient computation on neuromorphic hardware. In this thesis, I address the limitations of GD and BP with SNNs by proposing three algorithms. First, I extend a recent method that computes exact gradients with temporally-coded SNNs by relaxing the firing constraint of temporal coding and allowing multiple spikes per neuron. My proposed method generalizes the computation of exact gradients with SNNs and enhances the tradeoffs between performance and various other aspects of spiking neurons. Next, I introduce a novel alternative to BP that computes low-variance gradient estimates in a local and online manner. Compared to other alternatives to BP, the proposed method demonstrates an improved convergence rate and increased performance with DNNs. Finally, I combine these two methods and propose an algorithm that estimates gradients with SNNs in a manner that is compatible with the constraints of neuromorphic hardware. My empirical results demonstrate the effectiveness of the resulting algorithm in training SNNs without performing BP

    Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

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    This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Data-assisted modeling of complex chemical and biological systems

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    Complex systems are abundant in chemistry and biology; they can be multiscale, possibly high-dimensional or stochastic, with nonlinear dynamics and interacting components. It is often nontrivial (and sometimes impossible), to determine and study the macroscopic quantities of interest and the equations they obey. One can only (judiciously or randomly) probe the system, gather observations and study trends. In this thesis, Machine Learning is used as a complement to traditional modeling and numerical methods to enable data-assisted (or data-driven) dynamical systems. As case studies, three complex systems are sourced from diverse fields: The first one is a high-dimensional computational neuroscience model of the Suprachiasmatic Nucleus of the human brain, where bifurcation analysis is performed by simply probing the system. Then, manifold learning is employed to discover a latent space of neuronal heterogeneity. Second, Machine Learning surrogate models are used to optimize dynamically operated catalytic reactors. An algorithmic pipeline is presented through which it is possible to program catalysts with active learning. Third, Machine Learning is employed to extract laws of Partial Differential Equations describing bacterial Chemotaxis. It is demonstrated how Machine Learning manages to capture the rules of bacterial motility in the macroscopic level, starting from diverse data sources (including real-world experimental data). More importantly, a framework is constructed though which already existing, partial knowledge of the system can be exploited. These applications showcase how Machine Learning can be used synergistically with traditional simulations in different scenarios: (i) Equations are available but the overall system is so high-dimensional that efficiency and explainability suffer, (ii) Equations are available but lead to highly nonlinear black-box responses, (iii) Only data are available (of varying source and quality) and equations need to be discovered. For such data-assisted dynamical systems, we can perform fundamental tasks, such as integration, steady-state location, continuation and optimization. This work aims to unify traditional scientific computing and Machine Learning, in an efficient, data-economical, generalizable way, where both the physical system and the algorithm matter

    Fluidic Nozzles for Automotive Washer Systems: Computational Fluid Dynamics and Experimental Analysis

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    One of the main goals of this project was to cultivate an understanding of fluidic nozzle geometries and characteristic flow. Through this knowledge, three new fluidic nozzle concepts were developed to be used as components in several windscreen washer systems for an automotive part supplier, Kautex Textron CVS Ltd.Accurate and conclusive visualisation of flow through fluidic nozzles was vital in understanding how they can be best utilised for different applications. Over the past century, the specific needs of automotive cleaning systems have greatly developed with new technological discoveries, these advances allow the driver further knowledge of their surroundings. These specialised systems each require a different type of maintenance and cleaning system depending on their usage and the different size and shape of the vehicle. By completing this project, it is hoped to allow manufacturers to accurately identify what sort of fluidic nozzles are best for windscreen cleaning systems for a vehicle and how to design a nozzle to suit their specification. Fluidic nozzles have been researched experimentally and computationally to ensure an accurate comparison of results. By guaranteeing a precise comparison it will negate the need for high volume testing of nozzles in experimental situations, greatly reducing time and resources required to analyse a fluidic nozzle.The fluidic nozzles that are investigated and developed in this project were modelled and examined both experimentally and computationally, this ensured valid and accurate results were achieved by both the computational modelling and experimental testing. The development of the nozzles within this project was conducted using several experimental and computational setups to analyse the spray distribution, angle and oscillatory frequency amongst other parameters significant to the nozzle usage on a vehicle. Through this it was possible to tailor nozzle dimensions to allow for a streamlined design approach, this increased efficiency in fluidic nozzle development for any specification given by a vehicle manufacturing company customer. In addition to this the water flow emitted from the outlet was experimentally tested and modelled with both stationary and high surrounding velocities to examine how external variables affect the flow of the water from the nozzle.iiiThis project has been useful in the design manufacturing process of fluidic nozzles, by utilising computational modelling it has allowed a faster and cheaper method of analysing the effect of design alterations to fluidic nozzles. There is a greatly reduced frequency required for rapid prototyping of an array of fluidic chips with minimal dimensional differences to be used in the experimental stages of design, as once the inlet boundary conditions are established the nozzle can be redesigned completely within reason without the need for additional material wastage. This ensures a more easy and precise method of testing the manufacturing tolerances of a fluidic nozzle with a target of reaching customer specifications are always achieved.Three nozzles were aimed developed to satisfy conditions set by the customers, the vehicle manufacturers at which the new nozzle designs are aimed at are Honda, Nissan and Toyota. The nozzles to be established were designed for use on windscreen washer systems with a varying number of nozzles and with diverse windscreen sizes for different vehicles, resulting in a wide variety of specifications that must be met for each vehicle manufacturer. This meant that a single nozzle could not be utilised for all vehicles, instead a base model of fluidic chip was developed for the Nissan vehicle which was then dimensionally changed to suit the other vehicles.Throughout this project there were design specifications changes and ambiguities from the automotive company customers, leading to redesigns of the fluidic chips designed in this project. This means that although only two of the three fluidic nozzle designs are successfully in production, a much greater understanding of the mechanics of the fluid flow within the fluidic nozzle was achieved

    Contactless excitation for electric machines: high temperature superconducting flux pumps

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    With the intensification of global warming and climate change, the pace of transformation to a neutral-emission society is accelerating. In various sectors, electrification has become the absolute tendency to promote such a movement, where electric machines play an important role in the current power generation system. It is widely convinced that electric machines with very high power density are essential for future applications, which, however, can be hardly achieved by conventional technologies. Owing to the maturation of the second generation (2G) high temperature superconducting (HTS) technologies, it has been recognized that superconducting machine could be a competitive candidate to realize the vision. One significant obstacle that hinders the implementation of superconducting machines is how to provide the required magnetic fields, or in other words, how to energise them appropriately. Conventional direct injection is not suitable for HTS machines, because the current leads would bridge ambident temperature to the cryogenic environment, which can impose considerable heat load on the system and increase the operational cost. Thus, an efficient energisation method is demanded by HTS machines. As an emerging technology that can accumulate substantial flux in a closed loop without any physical contact, HTS flux pumps have been proposed as a promising solution. Among the existing developed HTS flux pumps, rotary HTS flux pumps, or so-called HTS dynamo, can output non-zero time-averaged DC voltage and charge the rest of the circuit if a closed loop has been formed. This type of flux pump is often employed together with HTS coils, where the HTS coils can potentially work in the persistent current mode, and act like electromagnets with a considerable magnetic field, having a wide range of applications in industry. The output characteristics of rotary HTS flux pumps have been extensively explored through experiments and finite element method (FEM) simulations, yet the work on constructing statistical models as an alternative approach to capture key characteristics has not been studied. In this thesis, a 2D FEM program has been developed to model the operation of rotary HTS flux pumps and evaluate the effects of different factors on the output voltage through parameter sweeping and analysis of variance. Typical design considerations, including the operating frequency, air gap, HTS tape width, and remanent flux density have been investigated, in particular, the bilateral effect of HTS tape width has been discovered and explained by looking at the averaged integration of the electric field over the HTS tape. Based on the data obtained from various simulations, regression analysis has been conducted through a collection of machine learning methods. It has been demonstrated that the output voltage of a rotary HTS flux pump can be obtained promptly with satisfactory accuracy via Gaussian process regression, aiming to provide a novel approach for future research and a powerful design tool for industrial applications using rotary HTS flux pumps. To enhance the applicability of the proposed statistical models, an updated FEM program has been built to take more parameters into account. The newly added parameters, namely the rotor radius and the width of permanent magnet, together with formerly included ones, should have covered all the key design parameters for a rotary HTS flux pump. Based on data collected from the FEM model, a well-trained semi-deep neural network (DNN) model with a back-propagation algorithm has been put forward and validated. The proposed DNN model is capable of quantifying the output voltage of a rotary HTS flux pump instantly with an overall accuracy of 98% with respect to the simulated values with all design parameters explicitly specified. The model possesses a powerful ability to characterize the output behaviour of rotary HTS flux pumps by integrating all design parameters, and the output characteristics of rotary HTS flux pumps have been successfully demonstrated and visualized using this model. Compared to conventional time-consuming FEM-based numerical models, the proposed DNN model has the advantages of fast learning, accurate computation, as well as strong programmability. Therefore, the DNN model can greatly facilitate the design and optimization process for rotary HTS flux pumps. An executable application has been developed accordingly based on the DNN model, which is believed to provide a useful tool for learners and designers of rotary HTS flux pumps. A new variant inspired by the working principles of rotary HTS flux pumps has been proposed and termed as stationary wave HTS flux pumps. The superiority of this type is that it has a simple structure without any moving components, and it utilises a controllable current-driven electromagnet to provide the required magnetic field. It has been demonstrated that the origin of the output voltage is determined by the asymmetric distribution of the dynamic resistance in the HTS tape, for which the electromagnet must be placed at such a position that its central line is not aligned with that of the HTS tape. A numerical model has been built to simulate the operation of a stationary wave HTS flux pump, based on which the output characteristics and dynamic resistance against various parameters have been investigated. Besides, accurate and reliable statistical models have been proposed to predict the open circuit voltage and effective dynamic resistance by adapting the previously developed machine learning techniques. The work presented in this PhD thesis can bring more insight into HTS flux pumps as an emerging promising contactless energisation technology, and the proposed statistical models can be particularly useful for the design and optimization of such devices
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