29 citations · 70 across the 18 of their papers we have counts for
18 papers
Large Deviations for Accelerating Neural Networks Training
Sreelekha Guggilam, Varun Chandola, Abani Patra
Artificial neural networks (ANNs) require tremendous amount of data to train on. However, in classification models, most data features are often similar which can lead to increase…
An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds
Amol Salunkhe, Georgios Georgalis, Abani Patra +1
Complete computation of turbulent combustion flow involves two separate steps: mapping reaction kinetics to low-dimensional manifolds and looking-up this approximate manifold durin…
Combined Data and Deep Learning Model Uncertainties: An Application to the Measurement of Solid Fuel Regression Rate
Georgios Georgalis, Kolos Retfalvi, Paul E. DesJardin +1
In complex physical process characterization, such as the measurement of the regression rate for solid hybrid rocket fuels, where both the observation data and the model used have…
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle
Sreelekha Guggilam, Varun Chandola, Abani Patra
Most current anomaly detection methods suffer from the curse of dimensionality when dealing with high-dimensional data. We propose an anomaly detection algorithm that can scale to…
Measurement of Hybrid Rocket Solid Fuel Regression Rate for a Slab Burner using Deep Learning
Gabriel Surina, Georgios Georgalis, Siddhant S. Aphale +2
This study presents an imaging-based deep learning tool to measure the fuel regression rate in a 2D slab burner experiment for hybrid rocket fuels. The slab burner experiment is de…
A Forward Backward Greedy approach for Sparse Multiscale Learning
Prashant Shekhar, Abani Patra
Multiscale Models are known to be successful in uncovering and analyzing the structures in data at different resolutions. In the current work we propose a feature driven Reproducin…
ALPS: A Unified Framework for Modeling Time Series of Land Ice Changes
Prashant Shekhar, Beata Csatho, Tony Schenk +2
Modeling time series is a research focus in cryospheric sciences because of the complexity and multiscale nature of events of interest. Highly non-uniform sampling of measurements…
First application of the failure forecast method to the GPS horizontal displacement data collected in the Campi Flegrei caldera (Italy) in 2011-2020
Andrea Bevilacqua, Abani Patra, E. Bruce Pitman +8
Using the failure forecast method we describe a first assessment of failure time on present-day unrest signals at Campi Flegrei caldera (Italy) based on the horizontal deformation…
Hierarchical regularization networks for sparsification based learning on noisy datasets
Prashant Shekhar, Abani Patra
We propose a hierarchical learning strategy aimed at generating sparse representations and associated models for large noisy datasets. The hierarchy follows from approximation spac…
Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data (Revised)
Sreelekha Guggilam, Syed M. A. Zaidi, Varun Chandola +1
Most current clustering based anomaly detection methods use scoring schema and thresholds to classify anomalies. These methods are often tailored to target specific data sets with…
Modernizing Titan2D, a Parallel AMR Geophysical Flow Code to Support Multiple Rheologies and Extendability
Nikolay A. Simakov, Renette L. Jones-Ivey, Ali Akhavan-Safaei +3
In this work, we report on strategies and results of our initial approach for modernization of Titan2D code. Titan2D is a geophysical mass flow simulation code designed for modelin…
Hierarchical Data Reduction and Learning
Prashant Shekhar, Abani Patra
This paper describes a hierarchical learning strategy for generating sparse representations of multivariate datasets. The hierarchy arises from approximation spaces considered at s…
Bayesian Anomaly Detection Using Extreme Value Theory
Sreelekha Guggilam, S. M. Arshad Zaidi, Varun Chandola +1
Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is…
Comparative analysis of the structures and outcomes of geophysical flow models and modeling assumptions using uncertainty quantification
Abani K. Patra, Andrea Bevilacqua, Ali Akhavan-Safaei +3
We present a new statistically driven method for analyzing the modeling of geophysical flows. Many models have been advocated by different modelers for such flows incorporating dif…
Probabilistic enhancement of the Failure Forecast Method using a stochastic differential equation and application to volcanic eruption forecasts
Andrea Bevilacqua, E. Bruce Pitman, Abani Patra +3
We introduce a doubly stochastic method for performing material failure theory based forecasts of volcanic eruptions. The method enhances the well known Failure Forecast Method equ…
Tracking System Behaviour from Resource Usage Data
Niyazi Sorkunlu, Varun Chandola, Abani Patra
Resource usage data, collected using tools such as TACC Stats, capture the resource utilization by nodes within a high performance computing system. We present methods to analyze t…
Report on the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3)
Daniel S. Katz, Sou-Cheng T. Choi, Kyle E. Niemeyer +18
This report records and discusses the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3). The report includes a description of the keynote prese…
Refinement on non-hydrostatic shallow granular flow model in a global Cartesian coordinate system
L. Yuan, W. Liu, J. Zhai +3
Current shallow granular flow models suited to arbitrary topography can be divided into two types, those formulated in bed-fitted curvilinear coordinates, and those formulated in g…