Projects

Develop an energy-efficient spiking neural network (SNN) computing architecture and software system capable of autonomous learning and operation

Developing a ground-based, quantum-secured, authenticated time distribution system for the energy grid.

PISCEES is a SciDAC Earth System Modeling project with the following goals: (1) To develop and apply robust, accurate, and scalable dynamical cores for ice sheet modeling on structured and unstructured meshes with adaptive refinement, (2) To evaluate ice sheet models using new tools and data sets for verification and validation (V&V) and uncertainty quantification (UQ), (3) to integrate these models and tools into DOE's Accelerated Climate Model for Energy (ACME). Using improved estimates of ice sheet initial conditions, we will simulate decade-to-century-scale evolution of the Greenland and Antarctic ice sheets, running PISCEES ice sheet models both in standalone mode and coupled to ACME. We aim to provide useful, credible predictions, including uncertainty ranges, of future ice-sheet mass loss and resulting changes in climate and sea level.
PISCEES is jointly funded by the Office of Biological and Environmental Research (BER) and the Office of Advanced Scientific Computing Research (ASCR) of the DOE Office of Science.
Principle Investigator: Steve Price - LANL and Esmond Ng – LBNL, Kate Evans - ORNL site PI

MULTISCALE is a SciDAC Earth System Modeling project with the primary goal of producing better climate models to serve as the scientific tools and predictive tools that will address the needs of both the climate sciences and policy-oriented communities. Here are notable features of MULTISCALE: (1) Grand challenges in projecting the future of the Earth's climate result from the interactions among small-scale features and large-scale structures of the ocean and atmosphere in climate models. (2) A generation of models that capture the structure and evolution of the climate system across a broad range of spatial and temporal scales is required. MULTISCALE is an integrated team of climate and computational scientists working to accelerate the development and integration of multiscale atmospheric and oceanic parameterizations into the Community Earth System Model (CESM).
MULTISCALE is jointly funded by the Biological and Environmental Research (BER) and Advanced Scientific Computing Research (ASCR) programs in the U.S. Department of Energy Office of Science.

Principle Investigator: Bill Collins - LBNL, Jim Hack - ORNL site PI

The initial part of our research will employ atmospheric observational data and focus on (a) objectively characterizing large-scale meteorological patterns (LMPs) associated with different classes of extreme weather events (EWEs) and (b) developing simple and concise atmospheric circulation metrics for identifying such LMPs in gridded datasets. We expect that atmospheric blocking will play a critical role in several EWE classes. 
Secondly, our project consists of a detailed diagnosis of the physical mechanisms by which (a) the implicated LMPs arise, (b) LMPs enact EWEs and (c) natural modes of climate variability (i.e., planetary-scale climate modes [PCMs]) serve to modulate LMPs/EWEs. The diagnostic results will be used to construct a set of dynamical metrics and incorporate them into a formal evaluation process to be used in DOE model development efforts. 
The third part of our project is the parallel study of EWEs, LMPs and PCMs in simulations of historical and future climate. There is particular interest in studying the benefits gained through enhanced model resolution by examining coupled model simulations conducted at ORNL.

Funding Source: DOE BER RGCM program
Principle Investigator: Rob Black - Georgia Tech, Kate Evans - ORNL PI

The Accelerated Climate Modeling for Energy (ACME) project is a newly launched project sponsored by the Earth System Modeling (ESM) program within U.S. Department of Energy's (DOE’s) Office of Biological and Environmental Research. ACME is an unprecedented collaboration among eight national laboratories and six partner institutions to develop and apply the most complete, leading-edge climate and Earth system models to challenging and demanding climate-change research imperatives. It is the only major national modeling project designed to address DOE mission needs to efficiently utilize DOE leadership computing resources now and in the future. While the project capabilities will address the critical science questions, its modeling system and related capabilities also can be flexibly applied by the DOE research community to address mission-specific climate change applications from U.S. Energy Sector Vulnerabilities to Climate Change and Extreme Weather.

Quantum computing promises a platform for efficiently solving certain types problems thought to be intractable for traditional computers. The number of qubits needed to be competitive with classical computers varies dramatically depending on the problem. This project seeks to determine the maximum quantum operation rate for a given cooling capacity.

Developing a ground-based, quantum-secured, authenticated time distribution system for the energy grid.

The Oak Ridge National Laboratory's Computational Data Analytics Group's has worked over 12 years in creating text analytics systems to quickly discover meaningful information from raw data. These capabilities focus on six key areas, emphasizing high performance over very large sets of raw documents.

Collecting and Extracting: Collecting millions of documents from databases, Internet, Social Media, and hard drives; extracting text from hundreds of file formats; and translating this information into multiple languages.
Storing and Indexing: Storing and indexing millions of documents in search servers, distributed file systems (MapReduce), relational databases, and file systems.
Recommending: Filtering the full content of millions of documents to recommend the most valuable and relevant information based on a user’s own information, or user selections, or a user’s interactions with information.
Categorize: Grouping items based on the full content of documents using supervised and semi-supervised machine learning methods and targeted search lists.
Clustering: Creating a hierarchical group of documents based on similarity using unsupervised learning methods on the full content of each document.
Visualizing: Showing hierarchies, groups, and relationships among documents that helps the user quickly understand their value, and to see new connections.


This work has resulted in eight issued ( 7,072,883 7,315,858 7,693,903 7,805,446 7,937,389 8,473,314 8,825,710 9,256,649) and one pending patents , several commercial licenses (including Pro2Serve and TextOre), a spin off company (Global Security Information Analysts LLC (GSIA)), an R&D 100 Awards, and scores of peer reviewed research publications.

Situ combines anomaly detection and data visualization to provide a distributed, streaming platform for discovery and explanation of suspicious behavior to enhance situation awareness.