491 research outputs found

    Data-driven deep-learning methods for the accelerated simulation of Eulerian fluid dynamics

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    Deep-learning (DL) methods for the fast inference of the temporal evolution of ļ¬‚uid-dynamics systems, based on the previous recognition of features underlying large sets of ļ¬‚uid-dynamics data, have been studied. Speciļ¬cally, 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 oļ¬€er higher freedom on how data is organised and processed. This motivated the use of graphs to represent the state of ļ¬‚uid-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 ļ¬‚uid-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 deļ¬ned from the original graph, and messages are also passed from ļ¬ner to coarser graphs and vice-versa. The low-resolution graphs allowed for eļ¬ƒciently capturing physics encompassing a range of lengthscales. Advection and ļ¬‚uid ļ¬‚ow, 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 suļ¬ƒcient to achieve high generalisation accuracy in advection simulations, ļ¬‚ow simulation highly beneļ¬ted 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 authorā€™s knowledge, the REMuS-GNN architecture was the ļ¬rst 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

    Adaptive Discontinuous Galerkin Methods for Variational Inequalities with Applications to Phase Field Models

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    Solutions of variational inequalities often have limited regularity. In particular, the nonsmooth parts are local, while other parts of the solution have higher regularity. To overcome this limitation, we apply hp-adaptivity, which uses a combination of locally finer meshes and varying polynomial degrees to separate the different features of the the solution. For this, we employ Discontinuous Galerkin (DG) methods and show some novel error estimates for the obstacle problem which emphasize the use in hp-adaptive algorithms. Besides this analysis, we present how to efficiently compute numerical solutions using error estimators, fast algebraic solvers which can also be employed in a parallel setup, and discuss implementation details. Finally, some numerical examples and applications to phase field models are presented

    Advanced BEM-based methodologies to identify and simulate wave fields in complex geostructures

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    To enhance the applicability of BEM for geomechanical modeling numerically optimized BEM models, hybrid FEM-BEM models, and parallel computation of seismic Full Waveform Inversion (FWI) in GPU are implemented. Inverse modeling of seismic wave propagation in inhomogeneous and heterogeneous half-plane is implemented in Boundary Element Method (BEM) using Particle Swarm Optimization (PSO). The Boundary Integral Equations (BIE) based on the fundamental solutions for homogeneous elastic isotropic continuum are modified by introducing mesh-dependent variables. The variables are optimized to obtain the site-specific impedance functions. The PSO-optimized BEM models have significantly improved the efficiency of BEM for seismic wave propagation in arbitrarily inhomogeneous and heterogeneous media. Similarly, a hybrid BEM-FEM approach is developed to evaluate the seismic response of a complex poroelastic soil region containing underground structures. The far-field semi-infinite geological region is modeled via BEM, while the near-field finite geological region is modeled via FEM. The BEM region is integrated into the global FEM system using an equivalent macro-finite-element. The model describes the entire wave path from the seismic source to the local site in a single hybrid model. Additionally, the computational efficiency of time domain FWI algorithm is enhanced by parallel computation in CPU and GPU

    Algebraic, Block and Multiplicative Preconditioners based on Fast Tridiagonal Solves on GPUs

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    This thesis contributes to the field of sparse linear algebra, graph applications, and preconditioners for Krylov iterative solvers of sparse linear equation systems, by providing a (block) tridiagonal solver library, a generalized sparse matrix-vector implementation, a linear forest extraction, and a multiplicative preconditioner based on tridiagonal solves. The tridiagonal library, which supports (scaled) partial pivoting, outperforms cuSPARSE's tridiagonal solver by factor five while completely utilizing the available GPU memory bandwidth. For the performance optimized solving of multiple right-hand sides, the explicit factorization of the tridiagonal matrix can be computed. The extraction of a weighted linear forest (union of disjoint paths) from a general graph is used to build algebraic (block) tridiagonal preconditioners and deploys the generalized sparse-matrix vector implementation of this thesis for preconditioner construction. During linear forest extraction, a new parallel bidirectional scan pattern, which can operate on double-linked list structures, identifies the path ID and the position of a vertex. The algebraic preconditioner construction is also used to build more advanced preconditioners, which contain multiple tridiagonal factors, based on generalized ILU factorizations. Additionally, other preconditioners based on tridiagonal factors are presented and evaluated in comparison to ILU and ILU incomplete sparse approximate inverse preconditioners (ILU-ISAI) for the solution of large sparse linear equation systems from the Sparse Matrix Collection. For all presented problems of this thesis, an efficient parallel algorithm and its CUDA implementation for single GPU systems is provided

    Multi-GPU aggregation-based AMG preconditioner for iterative linear solvers

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    We present and release in open source format a sparse linear solver which efficiently exploits heterogeneous parallel computers. The solver can be easily integrated into scientific applications that need to solve large and sparse linear systems on modern parallel computers made of hybrid nodes hosting NVIDIA Graphics Processing Unit (GPU) accelerators. The work extends our previous efforts in the exploitation of a single GPU accelerator and proposes an implementation, based on the hybrid MPI-CUDA software environment, of a Krylov-type linear solver relying on an efficient Algebraic MultiGrid (AMG) preconditioner already available in the BootCMatchG library. Our design for the hybrid implementation has been driven by the best practices for minimizing data communication overhead when multiple GPUs are employed, yet preserving the efficiency of the single GPU kernels. Strong and weak scalability results on well-known benchmark test cases of the new version of the library are discussed. Comparisons with the Nvidia AmgX solution show an improvement of up to 2.0x in the solve phase

    A Fully Parallelized and Budgeted Multi-level Monte Carlo Framework for Partial Differential Equations: From Mathematical Theory to Automated Large-Scale Computations

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    All collected data on any physical, technical or economical process is subject to uncertainty. By incorporating this uncertainty in the model and propagating it through the system, this data error can be controlled. This makes the predictions of the system more trustworthy and reliable. The multi-level Monte Carlo (MLMC) method has proven to be an effective uncertainty quantification tool, requiring little knowledge about the problem while being highly performant. In this doctoral thesis we analyse, implement, develop and apply the MLMC method to partial differential equations (PDEs) subject to high-dimensional random input data. We set up a unified framework based on the software M++ to approximate solutions to elliptic and hyperbolic PDEs with a large selection of finite element methods. We combine this setup with a new variant of the MLMC method. In particular, we propose a budgeted MLMC (BMLMC) method which is capable to optimally invest reserved computing resources in order to minimize the model error while exhausting a given computational budget. This is achieved by developing a new parallelism based on a single distributed data structure, employing ideas of the continuation MLMC method and utilizing dynamic programming techniques. The final method is theoretically motivated, analyzed, and numerically well-tested in an automated benchmarking workflow for highly challenging problems like the approximation of wave equations in randomized media

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    Machine learning algorithms for fluid mechanics

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    Neural networks have become increasingly popular in the field of fluid dynamics due to their ability to model complex, high-dimensional flow phenomena. Their flexibility in approximating continuous functions without any preconceived notion of functional form makes them a suitable tool for studying fluid dynamics. The main uses of neural networks in fluid dynamics include turbulence modelling, flow control, prediction of flow fields, and accelerating high-fidelity simulations. This thesis focuses on the latter two applications of neural networks. First, the application of a convolutional neural network (CNN) to accelerate the solution of the Poisson equation step in the pressure projection method for incompressible fluid flows is investigated. The CNN learns to approximate the Poisson equation solution at a lower computational cost than traditional iterative solvers, enabling faster simulations of fluid flows. Results show that the CNN approach is accurate and efficient, achieving significant speedup in the Taylor-Green Vortex problem. Next, predicting flow fields past arbitrarily-shaped bluff bodies from point sensor and plane velocity measurements using neural networks is focused on. A novel conformal-mapping-aided method is devised to embed geometry invariance for the outputs of the neural networks, which is shown to be critical for achieving good performance for flow datasets incorporating a diverse range of geometries. Results show that the proposed methods can accurately predict the flow field, demonstrating excellent agreement with simulation data. Moreover, the flow field predictions can be used to accurately predict lift and drag coefficients, making these methods useful for optimizing the shape of bluff bodies for specific applications.Open Acces

    Composable code generation for high order, compatible finite element methods

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    It has been widely recognised in the HPC communities across the world, that exploiting modern computer architectures, including exascale machines, to a full extent requires software commu- nities to adapt their algorithms. Computational methods with a high ratio of floating point op- erations to bandwidth are favorable. For solving partial differential equations, which can model many physical problems, high order finite element methods can calculate approximations with a high efficiency when a good solver is employed. Matrix-free algorithms solve the corresponding equations with a high arithmetic intensity. Vectorisation speeds up the operations by calculating one instruction on multiple data elements. Another recent development for solving partial differential are compatible (mimetic) finite ele- ment methods. In particular with application to geophysical flows, compatible discretisations ex- hibit desired numerical properties required for accurate approximations. Among others, this has been recognised by the UK Met office and their new dynamical core for weather and climate fore- casting is built on a compatible discretisation. Hybridisation has been proven to be an efficient solver for the corresponding equation systems, because it removes some inter-elemental coupling and localises expensive operations. This thesis combines the recent advances on vectorised, matrix-free, high order finite element methods in the HPC community on the one hand and hybridised, compatible discretisations in the geophysical community on the other. In previous work, a code generation framework has been developed to support the localised linear algebra required for hybridisation. First, the framework is adapted to support vectorisation and further, extended so that the equations can be solved fully matrix-free. Promising performance results are completing the thesis.Open Acces

    Scalable computational algorithms for geospatial COVID-19 spread using high performance computing

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    A nonlinear partial differential equation (PDE) based compartmental model of COVID-19 provides a continuous trace of infection over space and time. Finer resolutions in the spatial discretization, the inclusion of additional model compartments and model stratifications based on clinically relevant categories contribute to an increase in the number of unknowns to the order of millions. We adopt a parallel scalable solver that permits faster solutions for these high fidelity models. The solver combines domain decomposition and algebraic multigrid preconditioners at multiple levels to achieve the desired strong and weak scalabilities. As a numerical illustration of this general methodology, a five-compartment susceptible-exposed-infected-recovered-deceased (SEIRD) model of COVID-19 is used to demonstrate the scalability and effectiveness of the proposed solver for a large geographical domain (Southern Ontario). It is possible to predict the infections for a period of three months for a system size of 186 million (using 3200 processes) within 12 hours saving months of computational effort needed for the conventional solvers
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