arXiv subjects

Benjamin Pachev

Publications and source records attributed to Benjamin Pachev.

5 recordsLinked to original sources

arXiv2026

Multi-GPU Acceleration of Interface Problems in FEniCS, with Application to Cardiac Modeling

GPU acceleration of finite element assembly is well established for standard formulations, but mixed-dimensional problems, in which volumetric subdomains are coupled via lower-dimensional interfaces, remain unsupported in automated frameworks such as FEniCS. We extend cuDOLFINx, a GPU-enabled extension of the DOLFINx finite element framework, to assemble such interface problems on GPUs while preserving the high-level FEniCS programming model. Combined with GPU-resident Krylov solvers and algebraic multigrid preconditioning, this enables end-to-end GPU execution of the EMI (Extracellular--Membrane--Intracellular) model, an elliptic interface problem from cardiac modeling in which many intracellular subdomains are coupled through membranes. We compare the GPU implementation against a CPU-based one through strong and weak scaling experiments on regular and realistic 3D cardiac geometries with up to 8000 cells and 22 million degrees of freedom. In terms of assembly, a single GH200 node is roughly equivalent to two 144-core CPU nodes in strong scaling, and to two to three nodes in weak scaling, depending on geometry. For the linear solver, performance depends on geometry, preconditioning, and problem scale. On the regular geometry, GPU advantages emerge mainly at larger problem sizes and node counts. On the realistic geometry, with the same algebraic multigrid preconditioner for both architectures, the GPU is faster at all tested scales. The implementation is available as open-source software and applies to other problems with interface coupling.

math.NA↗

arXiv2026

Global Location-Invariant Peak Storm Surge Prediction

Storm surge is a significant threat to coastal communities across the globe, responsible for loss of life and enormous property damage. Consequently, significant efforts have been expended to develop high-fidelity physics-based models for storm surge prediction. However, such models are often extremely computationally expensive and require supercomputing resources. In recent years, there has been a growing trend towards data-driven surrogate models, which approximate the capabilities of high-fidelity models at a tiny fraction of the computational cost. Most datasets of high-fidelity storm surge model output are limited to narrow geographical regions, with the majority focused on the continental United States and China. This trend is reflected in the scope of existing storm surge surrogate models. In this work, we present a novel dataset for training storm surge surrogate models with global applicability. The dataset consists of high-resolution peak surge output from the ADvanced CIRCulation (ADCIRC) model for over 15,000 landfalling synthetic storms distributed across the world. To the author's knowledge, it is the largest dataset of its kind ever assembled, and is unique in its global scope. We additionally present a machine learning model for peak storm surge based on computer vision architecture. The model is trained on our new global dataset and can accurately predict maximum storm surge in disparate geographical regions - including those for which few or no surrogate models exist. Both the dataset and accompanying model are publicly available, with the aim to support the development of additional storm surge models with global reach.

cs.CE↗

arXiv2023

A Framework for Flexible Peak Storm Surge Prediction

Storm surge is a major natural hazard in coastal regions, responsible both for significant property damage and loss of life. Accurate, efficient models of storm surge are needed both to assess long-term risk and to guide emergency management decisions. While high-fidelity regional- and global-ocean circulation models such as the ADvanced CIRCulation (ADCIRC) model can accurately predict storm surge, they are very computationally expensive. Here we develop a novel surrogate model for peak storm surge prediction based on a multi-stage approach. In the first stage, points are classified as inundated or not. In the second, the level of inundation is predicted . Additionally, we propose a new formulation of the surrogate problem in which storm surge is predicted independently for each point. This allows for predictions to be made directly for locations not present in the training data, and significantly reduces the number of model parameters. We demonstrate our modeling framework on two study areas: the Texas coast and the northern portion of the Alaskan coast. For Texas, the model is trained with a database of 446 synthetic hurricanes. The model is able to accurately match ADCIRC predictions on a test set of synthetic storms. We further present a test of the model on Hurricanes Ike (2008) and Harvey (2017). For Alaska, the model is trained on a dataset of 109 historical surge events. We test the surrogate model on actual surge events including the recent Typhoon Merbok (2022) that take place after the events in the training data. For both datasets, the surrogate model achieves similar performance to ADCIRC on real events when compared to observational data. In both cases, the surrogate models are many orders of magnitude faster than ADCIRC.

cs.CE↗

arXiv2022

Concurrent multi-parameter learning demonstrated on the Kuramoto-Sivashinsky equation

We develop an algorithm based on the nudging data assimilation scheme for the concurrent (on-the-fly) estimation of scalar parameters for a system of evolutionary dissipative partial differential equations in which the state is partially observed. The algorithm takes advantage of the error that results from nudging a system with incorrect parameters with data from the true system. The intuitive nature of the algorithm makes its extension to several different systems immediate, and it allows for recovery of multiple parameters simultaneously. We test the method on the Kuramoto-Sivashinsky equation in one dimension and demonstrate its efficacy in this context.

math.NA↗

arXiv2017

Fast link prediction for large networks using spectral embedding

Many link prediction algorithms require the computation of a similarity metric on each vertex pair, which is quadratic in the number of vertices and infeasible for large networks. We develop a class of link prediction algorithms based on a spectral embedding and the k closest pairs algorithm that are scalable to very large networks. We compare the prediction accuracy and runtime of these methods to existing algorithms on several large link prediction tasks. Our methods achieve comparable accuracy to standard algorithms but are significantly faster.

cs.SI↗