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Found 8 papers, 3 papers with code
Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning
Automatic differentiation (AD) is a critical step in physics-informed machine learning, required for computing the high-order derivatives of network output w. r. t.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
Scientific Machine Learning Benchmarks
In this paper, we describe our approach to the development of scientific machine learning benchmarks and review other approaches to benchmarking scientific machine learning.
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights.
Data optimization for large batch distributed training of deep neural networks
Distributed training in deep learning (DL) is common practice as data and models grow.
DataFed: Towards Reproducible Research via Federated Data Management
The increasingly collaborative, globalized nature of scientific research combined with the need to share data and the explosion in data volumes present an urgent need for a scientific data management system (SDMS).
Databases Computers and Society
USID and Pycroscopy -- Open frameworks for storing and analyzing spectroscopic and imaging data
The second is Pycroscopy, which provides algorithms for scientific analysis of nanoscale imaging and spectroscopy modalities and is built on top of pyUSID and USID.
Data Analysis, Statistics and Probability
Defining Big Data Analytics Benchmarks for Next Generation Supercomputers
The design and construction of high performance computing (HPC) systems relies on exhaustive performance analysis and benchmarking.
Performance
