UMBC Mathematics and Statistics Department
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Data assimilation for the Navier-Stokes equations using local observables
(2020-08-16)We develop, analyze, and test an approximate, global data assimilation/synchronization algorithm based on purely local observations for the two-dimensional Navier-Stokes equations on the torus. We prove that, for any error ... -
Error Estimates for Deep Learning Methods in Fluid Dynamics
In this study, we provide error estimates and stability analysis of deep learning techniques for certain partial differential equations including the incompressible Navier-Stokes equations. In particular, we obtain explicit ... -
Sparsity and Independence: Balancing Two Objectives in Optimization for Source Separation with Application to fMRI Analysis
(Elsevier, 2017-06-11)Because of its wide applicability in various disciplines, blind source separation (BSS), has been an active area of research. For a given dataset, BSS provides useful decompositions under minimum assumptions typically by ... -
Parallel Performance Studies for a 3-D Elliptic Test Problem on the 2018 Portion of the Taki Cluster
The new 2018 nodes in the CPU cluster taki in the UMBC High Performance Computing Facility contain two 18-core Intel Skylake CPUs and 384 GB of memory per node, connected by an EDR (Enhanced Data Rate) InfiniBand interconnect. ... -
Parallel Performance Studies for an Elliptic Test Problem on the 2018 Portion of the Taki Cluster
The new 2018 nodes in the CPU cluster taki in the UMBC High Performance Computing Facility contain two 18-core Intel Skylake CPUs and 384 GB of memory per node, connected by an EDR (Enhanced Data Rate) InfiniBand interconnect. ... -
Mineral Dust Detection Using Satellite Data
Mineral dust, defined as aerosol originating from the soil, can have various harmful effects to the environment and human health. The detection of dust, and particularly incoming dust storms, may help prevent some of these ... -
Benchmarking parallel implementations of cloud type clustering from satellite data
The study of clouds, i.e., where they occur and what are their characteristics, plays a key role in the understanding of climate change. The aim of this project is to use machine learning in conjunction with parallel ... -
Hybrid MPI+OpenMP parallelization of image reconstruction in proton beam therapy on multi-core and many-core processors
The advantage of proton beam therapy is that the lethal dose of radiation is delivered by a sharp increase toward the end of the beam range, known as the Bragg peak (BP), with no dose delivered beyond. By using these ... -
Strong and Weak Scalability Studies for the 3-D Poisson Equation on the Taki 2018 Cluster
The new 2018 nodes in the cluster taki in the UMBC High Performance Computing Facility contain two 18-core Intel Skylake CPUs and 384 GB of memory per node, connected by an EDR (Enhanced Data Rate) InfiniBand interconnect. ... -
Deep Learning for Classification of Compton Camera Data in the Reconstruction of Proton Beams in Cancer Treatment
(UMBC, 2020-06-12)Real-time imaging has potential to greatly increase the effectiveness of proton beam therapy in cancer treatment. One promising method of real-time imaging is the use of a Compton camera to detect prompt gamma rays, which ... -
Daily Precipitation Generation using a Hidden Markov Model with Correlated Emissions for the Potomac River Basin
(UMBC, 2020-06-30)A daily precipitation generator based on a hidden Markov model with Gaussian copulas (HMM-GC) is constructed using remote sensing data from GPM-IMERG for the Potomac river basin on the East Coast of the USA. Daily ... -
A Comparison of Stochastic Precipitation Generation Models for the Potomac River Basin
Weather ensembles are an integral part of weather forecasting and can also be used to test the sensitivity and performance of climate models. Among meteorological variables, simultaneous simulation of precipitation at ... -
Parallel Hyperparameter Tuning of Accuracy for Deep Learning based Tornado Predictions
Predicting violent storms and dangerous weather conditions with current physics based weather models can take a long time due to the immense complexity associated with numerical simulations. Machine learning has the potential ... -
Using Machine Learning Techniques for Supercell Tornado Prediction with Environmental Sounding Data
Tornadoes pose a forecast challenge to National Weather Service forecasters because of their quick development and potential for life-threatening damage. The use of machine learning in severe weather forecasting has recently ... -
Use of Deep Learning to Classify Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy
Real-time imaging has potential to greatly increase the effectiveness of proton beam therapy for cancer treatment. One promising method of real-time imaging is the use of a Compton camera to detect prompt gamma rays, which ... -
Stochastic Precipitation Generation for the Potomac River Basin Using Hidden Markov Models
A daily precipitation generator based on hidden Markov models (HMM) using satellite precipitation estimates is studied for the Potomac river basin in Eastern USA over the wet season months of July to September. GPM-IMERG ... -
Performance Benchmarking of Parallel Hyperparameter Tuning for Deep Learning based Tornado Predictions
(UMBC, 2020-05-31)Predicting violent storms and dangerous weather conditions with current models can take a long time due to the immense complexity associated with weather simulation. Machine learning has the potential to classify tornadic ... -
Quantum Annealing Based Binary Compressive Sensing with Matrix Uncertainty
(2019-01-01)Compressive sensing is a novel approach that linearly samples sparse or compressible signals at a rate much below the Nyquist-Shannon sampling rate and outperforms traditional signal processing techniques in acquiring and ... -
Study of Exploiting Coarse-Grained Parallelism in Block-Oriented Numerical Linear Algebra Routines
(UMBC, 2020-06-17)We have developed streaming implementations of two numerical linear algebra operations that further exploit the block decomposition strategies commonly used in these operations to obtain performance. The implementations ... -
Tornado Storm Data Synthesization using Deep Convolutional Generative Adversarial Network: Related Works and Implementation Details
Predicting violent storms and dangerous weather conditions with current models can take a long time due to the immense complexity associated with weather simulation. Machine learning has the potential to classify tornadic ...
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