5 citations · 14 across the 12 of their papers we have counts for
12 papers
Analyzing the Performance of Deep Encoder-Decoder Networks as Surrogates for a Diffusion Equation
J. Quetzalcoatl Toledo-Marin, James A. Glazier, Geoffrey Fox
Neural networks (NNs) have proven to be a viable alternative to traditional direct numerical algorithms, with the potential to accelerate computational time by several orders of ma…
Does the Catalog of California Earthquakes, with Aftershocks Included, Contain Information about Future Large Earthquakes?
John B. Rundle, Andrea Donnellan, Geoffrey Fox +2
Yes. Interval statistics have been used to conclude that major earthquakes are random events in time and cannot be anticipated or predicted. Machine learning is a powerful new tech…
GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events
Bo Feng, Geoffrey Fox
Spatiotemporal time series nowcasting should preserve temporal and spatial dynamics in the sense that generated new sequences from models respect the covariance relationship from h…
Earthquake Nowcasting with Deep Learning
Geoffrey Fox, John Rundle, Andrea Donnellan +1
We review previous approaches to nowcasting earthquakes and introduce new approaches based on deep learning using three distinct models based on recurrent neural networks and trans…
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Steven Farrell, Murali Emani, Jacob Balma +40
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing syste…
Scientific Machine Learning Benchmarks
Jeyan Thiyagalingam, Mallikarjun Shankar, Geoffrey Fox +1
The breakthrough in Deep Learning neural networks has transformed the use of AI and machine learning technologies for the analysis of very large experimental datasets. These datase…
HPTMT Parallel Operators for High Performance Data Science & Data Engineering
Vibhatha Abeykoon, Supun Kamburugamuve, Chathura Widanage +5
Data-intensive applications are becoming commonplace in all science disciplines. They are comprised of a rich set of sub-domains such as data engineering, deep learning, and machin…
HPTMT: Operator-Based Architecture for Scalable High-Performance Data-Intensive Frameworks
Supun Kamburugamuve, Chathura Widanage, Niranda Perera +5
Data-intensive applications impact many domains, and their steadily increasing size and complexity demands high-performance, highly usable environments. We integrate a set of ideas…
Solving Newton's Equations of Motion with Large Timesteps using Recurrent Neural Networks based Operators
JCS Kadupitiya, Geoffrey C. Fox, Vikram Jadhao
Classical molecular dynamics simulations are based on solving Newton's equations of motion. Using a small timestep, numerical integrators such as Verlet generate trajectories of pa…
Understanding ML driven HPC: Applications and Infrastructure
Geoffrey Fox, Shantenu Jha
We recently outlined the vision of "Learning Everywhere" which captures the possibility and impact of how learning methods and traditional HPC methods can be coupled together. A pr…
Performance Optimization on Model Synchronization in Parallel Stochastic Gradient Descent Based SVM
Vibhatha Abeykoon, Geoffrey Fox, Minje Kim
Understanding the bottlenecks in implementing stochastic gradient descent (SGD)-based distributed support vector machines (SVM) algorithm is important in training larger data sets.…
A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures
Shantenu Jha, Judy Qiu, Andre Luckow +2
Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and schedu…