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The GES DIS is one of 12 NASA Earth science data centers. The GES DISC vision is to enable researchers and educators maximize knowledge of the Earth by engaging in understanding their goals, and by leading the advancement of remote sensing information services in response to satisfying their goals. This presentation will describe the GES DISC approach, successes, challenges, and best practices.
Buildings account for about 40 percent of the total energy consumption in the U.S. and emit approximately one third of greenhouse gas emissions. But they also offer tremendous potential for achieving significant greenhouse gas reductions with the right savings strategies. With an increasing amount of data from buildings and advanced computational and analytical abilities, buildings can be made “smart” to optimize energy consumption and occupant comfort. Smart buildings are often characterized as having a high degree of data and system integration, connectivity and control, as well as the advanced use of data analytics. These “smarts” can enable up to 10–20% savings in a building, and help ensure that they persist over time. In 2009, Microsoft Corporation launched the Energy-Smart Buildings (ESB) program with a vision to improve building operations services, security and accessibility in services, and new tenant applications and services that improve productivity and optimize energy use. The ESB program focused on fault diagnostics, advanced analytics and new organizational processes and practices to support their operational integration. In addition to the ESB program, Microsoft undertook capital improvement projects that made effective use of a utility incentive program and lab consolidations over the same duration. The ESB program began with a pilot at Microsoft's Puget Sound campus that identified significant savings of up to 6–10% in the 13 pilot buildings. The success of the pilot led to a global deployment of the program. Between 2009 and 2015, there was a 23.7% reduction in annual electricity consumption (kWh) at the Puget Sound campus with 18.5% of that resulting from the ESB and lab consolidations. This article provides the results of research conducted to assess the best-practice strategies that Microsoft implemented to achieve these savings, including the fault diagnostic routines that are the foundation of the ESB program and organizational change management practices. It also presents the process that was adopted to scale the ESB program globally. We conclude with recommendations for how these successes can be generalized and replicated by other corporate enterprises.
In the context of real-time optimization and model predictive control of industrial systems, machine learning, and neural networks represent cutting-edge tools that hold promise for enhancing dynamic modeling. This work presents a novel transformer neural network architecture for real-time optimization and model predictive control. This network design includes a modified attention mechanism inspired by positional embedding attention from vision transformers and task-specific modifications to the input-output structure of the transformer’s decoder stack. Experiments were conducted using data from a 450 MW coal-fired power plant to evaluate this approach's effectiveness. The transformer neural network was compared with conventional recurrent models, including GRU and LSTM. The transformer exhibited a 6% increase in the R-squared (R2) value of predictions and an 83% reduction in mean squared error (MSE). Computation time was also reduced by 84% compared to conventional recurrent models.
Each year, Brookhaven National Laboratory (BNL) is required to provide a report of its completed Laboratory Directed Research and Development Program (LDRD) projects to the Department of Energy (DOE) Office of Scientific and Technical Information in accordance with DOE Order 413.2C Chg1 (MinChg) dated August 2, 2018. This report provides a detailed look at the scientific and technical activities for each of the LDRD projects funded by BNL in FY 2022, in fulfillment of that requirement. In FY 2022, the BNL LDRD Program funded 70 projects, 30 of which were new starts, at a total cost of $17.2M. The investments that BNL makes in its LDRD program support the Laboratory’s strategic goals. BNL has identified seven scientific initiatives that define the Laboratory’s scientific future and that will enable it to realize its overall vision. This requires simultaneous excellence in all aspects of BNL’s work – from science and operations, to external partnerships with the local, state, and national communities, and beyond. This is enabled by safe, efficient, and secure operations; by an unwavering commitment to a diverse, equitable, and inclusive environment, including workforce development, both with staff and reaching out to the community; and by a strong focus on renewed infrastructure. The seven scientific initiatives are: 1) Nuclear Physics: uncover the structure of visible matter by constructing and operating the Electron-Ion Collider at BNL to maintain international leadership in nuclear physics for decades; 2) Clean Energy and Climate: support a net-zero U.S. economy through fundamental research in basic energy and climate sciences to revolutionize grid-scale storage, renewable integration, and the study of atmospheric processes with a new facility to improve climate predictability; 3) Quantum Information Science and Technology: discover new quantum materials to enhance quantum computers and develop an entanglement sharing quantum network as a prototype for the first quantum internet; 4) Discovery Science Driven by the Human-AI Facility Integration: revolutionize the operation of experiments across the sciences at user facilities and in core programs; 5) High Energy Physics: understand the origin of space and time with the ATLAS high luminosity upgrade at CERN and the future Long Baseline Neutrino Facility/Deep Underground Neutrino Experiment; 6) Isotope Production: accelerate and expand isotope production to ensure the security of the Nation’s supply; 7) Accelerator Science and Technology: harness the cross-cutting accelerator science expertise at BNL to develop new facilities, improve and expand its user facilities, and promote the use of accelerators in industry. The funded projects support BNL’s seven scientific initiatives and priority programs as well as new areas of research and competencies at the Laboratory that are consistent with the Laboratory’s vision and mission. In total, these LDRD investments supported 43 postdoctoral researchers in whole or in part and resulted in 138 publications and 7 awards. This Program Activities Report represents the future of BNL science; it is an impressive body of exploratory work that investigates many scientific and technical directions in support of the DOE and BNL missions.
Efficient processing of information is crucial for the optimization of neural resources in both biological and artificial visual systems. In this paper, we study the efficiency that may be obtained via the use of a fovea. Using biologically-motivated agents, we study visual information processing, learning, and decision making in a controlled artificial environment, namely the Atari Pong video game. We compare the resources necessary to play Pong between agents with and without a fovea. Our study shows that a fovea can significantly reduce the neural resources, in the form of number of neurons, number of synapses, and number of computations, while at the same time maintaining performance at playing Pong. To our knowledge, this is the first study in which an agent must simultaneously optimize its visual system, along with its decision making and action generation capabilities. That is, the visual system is integral to a complete agent.
This work evaluates the efficiency calibrating of HpGe detector coupled with Canberra GC3018 with Genie 2000 software and Ortec GEM25-76-XLB-C with Gamma Vision software; available at Neutron activation analysis laboratory in Malaysian Nuclear Agency (NM). The efficiency calibration curve was constructed from measurement of an IAEA, standard gamma–point sources set composed by {sup 214}Am, {sup 57}Co, {sup 133}Ba, {sup 152}Eu, {sup 137}Cs and {sup 60}Co. The efficiency calibrations were performed for three different geometries: 5, 10 and 15 cm distances from the end cap detector. The polynomial parameters functions were simulated through a computer program, MATLAB in order to find an accurate fit to the experimental data points. The efficiency equation was established from the known fitted parameters which allow for the efficiency evaluation at particular energy of interest. The study shows that significant deviations in the efficiency, depending on the source-detector distance and photon energy.
This dataset release corresponds to the work conducted in ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling, where large-scale AI methods were applied to improve climate and weather resolution. The collection integrates four widely used, publicly available datasets: ERA5, PRISM, DAYMET, and IMERG. To prepare the data for ORBIT-2 model training and evaluation, we applied a preprocessing pipeline that generates paired low-resolution and high-resolution samples, enabling supervised downscaling experiments. The transformation from coarse to fine scales was performed using bilinear regridding, consistent with the procedures described in WeatherBench2, a community benchmark for weather and climate AI models. This dataset supports the development and evaluation of foundation models designed for weather and climate downscaling at exascale. Additional details on methodology and applications can be found in Wang et al., ORBIT-2 (arXiv:2505.04802, 2025).
In January 2010, the University of Minnesota, along with academic and industry project partners, began work on a four year project to establish new facilities and research in strategic areas of wind energy necessary to move the nation towards a goal of 20% wind energy by 2030. The project was funded by the U.S. Department of Energy with funds made available through the American Recovery and Reinvestment Act of 2009. $7.9M of funds were provided by DOE and $3.1M was provided through matching funds. The project was organized into three Project Areas. Project Area 1 focused on design and development of a utility scale wind energy research facility to support research and innovation. The project commissioned the Eolos Wind Research Field Station in November of 2011. The site, located 20 miles from St. Paul, MN operates a 2.5MW Clipper Liberty C-96 wind turbine, a 130-ft tall sensored meteorological tower and a robust sensor and data acquisition network. The site is operational and will continue to serve as a site for innovation in wind energy for the next 15 years. Project Areas 2 involved research on six distinct research projects critical to the 20% Wind Energy by 2030 goals. The research collaborations involved faculty from two universities, over nine industry partners and two national laboratories. Research outcomes include new knowledge, patents, journal articles, technology advancements, new computational models and establishment of new collaborative relationships between university and industry. Project Area 3 focused on developing educational opportunities in wind energy for engineering and science students. The primary outcome is establishment of a new graduate level course at the University of Minnesota called Wind Engineering Essentials. The seminar style course provides a comprehensive analysis of wind energy technology, economics, and operation. The course is highly successful and will continue to be offered at the University. The vision of U.S. DOE to establish unique, open-access research facilities and creation of university-industry research collaborations in wind energy were achieved through this project. The University of Minnesota, through the establishment of the Eolos Wind Energy Consortium and the Eolos Wind Research Field Station continue to develop new research collaborations with industry partners.
The Biological and Environmental Research (BER) program within the U.S. Department of Energy (DOE) Office of Science supports research focusing on the interconnections between energy production and the living environment. This fundamental research, conducted at universities, DOE national laboratories, and research institutions across the country, explores organisms and ecosystems that can influence the U.S. energy system and advances understanding of the relationships between energy and environment from local to global scales. Research within BER can be categorized into biological systems and Earth and environmental systems. Biological systems research seeks to characterize and predictively understand microbial and plant systems using genomic science, computational analyses, and experimental approaches. Foundational knowledge of the structure and function of these systems underpins the ability to leverage natural processes for energy production, including the sustainable development of biofuels and other bioproducts. Earth and environmental systems research seeks to characterize and predictively understand feedbacks between Earth and energy systems and includes studies on atmospheric physics and chemistry, ecosystem ecology and biogeochemistry, and development and validation of Earth system models extending from regional to global scales. To promote world-class research in these areas, BER supports user facilities that enable observation and measurement of atmospheric, biological, and biogeochemical processes using the latest technologies.
Dramatic technological advances in genomics continue to transform modern biology. The DOE Joint Genome Institute (DOE JGI) has been at the leading edge of large-scale sequence-based science from its inception. Presently our ability to generate genomic data greatly outpaces our capacity to convert these data into biological insights. Bridging the gap between a sequenced genome and understanding of organism-scale functions is a significant unsolved problem in modern biology. Consequently, as the DOE JGI plans for its future, a major objective is to couple the generation of sequence data with the development of new large-scale experimental and computational capabilities to functionally annotate DNA sequences, thereby narrowing the gap between generation and interpretation of sequence data.
The ever-increasing volumes of scientific data combined with sophisticated techniques for extracting information from them have led to the increasing popularity of ensemble workflows which are a collection of runs of individual workflows. A traditional approach followed by scientists to run ensembles is to rely on simple scripts to execute different runs and manage resources. This approach is not scalable and is error-prone, thereby motivating the development of workflow management systems that specialize in executing ensembles on HPC clusters. However, when the size of both the ensemble and the target system reach extreme scales, existing workflow management systems face new challenges that hamper their efficient execution. In this paper, we describe our experience scaling an ensemble workflow from the computational biology domain from the early design stages to the execution at extreme scale on Summit, a leadership class supercomputer at the Oak Ridge National Laboratory. We discuss challenges that arise when scaling ensembles to several million runs on thousands of HPC nodes. We identify challenges with composition of the ensemble itself, its execution at large scale, post-processing of the generated data, and scalability of the file system. Based on the experience acquired, we develop a generic vision of the capabilities and abstractions to add to existing workflow management systems to enable the execution of ensemble workflows at extreme scales. We believe that the understanding of these fundamental challenges will help application teams along with workflow system developers with designing the next generation of infrastructure for composing and executing extreme-scale ensemble workflows.
As a Jet Propulsion Laboratory astronomer, John D. Callahan developed a computer program called Multimission Interactive Planner (MIP) to help astronomers analyze scientific and optical data collected on the Voyager's Grand Tour. The commercial version of the program called XonVu is published by XonTech, Inc. Callahan has since developed two more advanced programs based on MIP technology, Grand Tour and Jovian Traveler, which simulate Voyager and Giotto missions. The software allows astronomers and space novices to view the objects seen by the spacecraft, manipulating perspective, distance and field of vision.
NASA has joined with the DOD to conduct the National Aerospace Plane (NASP) program, whose experimental test vehicle will be designated the X-30. NASP will study the X-30's takeoff from a runway under its own power, acceleration to high Mach number on the basis of airbreathing propulsion, emergence into LEO, reentry into the earth atmosphere, and descent to a powered horizontal landing. NASP will thereby generate technology base data for three distinct types of aircraft: upper-atmosphere hypersonic-cruise aircraft, LEO space transports, and military transatmospheric vehicles. The current concept-validation phase of NASP focuses on airbreathing propulsion, lightweight/high-strength heat-resistant materials, and computational fluid dynamics.
The role of vision in teleoperation has been recognized as an important element in the man-machine control loop. In most applications of remote manipulation, direct vision cannot be used. To overcome this handicap, the human operator's control capabilities are augmented by a television system. This medium provides a practical and useful link between workspace and the control station from which the operator perform his tasks. Human performance deteriorates when the images are degraded as a result of instrumental and transmission limitations. Image enhancement is used to bring out selected qualities in a picture to increase the perception of the observer. A general purpose digital computer, an extensive special purpose software system is used to perform an almost unlimited repertoire of processing operations.
Modem aircraft design involves study of airflow through both windtunnel testing and computer simulation. These computer simulations result in often very large and complex sets of numbers, which contain information critical to the aircrafts performance. This talk will describe how visualization is used to understand these simulations, using a variety of techniques including low-level analysis such as simulated particles, high-level feature detection, and virtual-reality-based techniques for exploration. We will focus on the challenges of extremely large data sets, interactive performance, and information extraction. The talk will close with a vision of the future including the integration of simulation and visualization.
The work reported had been undertaken to develop hardware that would guide an automatic vehicle in the exploration of the surface of Mars, recognize objects, and report the findings to earth. Approaches for automatically determining the range and shape of objects are discussed, giving attention to the automatic comparison of one scan line of the left with one scan line of the right TV image. Methods of mapping binocular space into match space are considered together with the use of a model in match space, rules for processing random-dot stereograms, the elimination of spurious matches, random-square stereograms, questions of the processing of a real scene, and aspects of range accuracy. Methods of computation for extracting other features are also discussed along with stereo TV cameras and approaches for reconstructing the appearance of a scene.
Spectro-polarimetric imaging in the long-wave infrared (LWIR) region plays a crucial role in applications from night vision and machine perception to trace gas sensing and thermography. However, the current generation of spectro-polarimetric LWIR imagers suffers from limitations in size, spectral resolution, and field of view (FOV). While meta-optics-based strategies for spectro-polarimetric imaging have been explored in the visible spectrum, their potential for thermal imaging remains largely unexplored. In this work, we introduce an approach for spectro-polarimetric decomposition by combining large-area stacked meta-optical devices with advanced computational imaging algorithms. The co-design of a stack of spinning dispersive metasurfaces along with compressive sensing and dictionary learning algorithms allows simultaneous spectral and polarimetric resolution without the need for bulky filter wheels or interferometers. Our spinning-metasurface-based spectro-polarimetric stack is compact (<10×10×10cm) and robust, and it offers a wide field of view (20.5°). We show that the spectral resolving power of our system substantially enhances performance in machine learning tasks such as material classification, a challenge for conventional panchromatic thermal cameras. Our approach represents a significant advance in the field of thermal imaging for a wide range of applications including heat-assisted detection and ranging (HADAR).
The KY DOE EPSCoR Program supports two research clusters. The Materials Cluster uses unique equipment and computational methods that involve research expertise at the University of Kentucky and University of Louisville. This team determines the physical, chemical and mechanical properties of nanostructured materials and examines the dominant mechanisms involved in the formation of new self-assembled nanostructures. State-of-the-art parallel computational methods and algorithms are used to overcome current limitations of processing that otherwise are restricted to small system sizes and short times. The team also focuses on developing and applying advanced microtechnology fabrication techniques and the application of microelectrornechanical systems (MEMS) for creating new materials, novel microdevices, and integrated microsensors. The second research cluster concentrates on High Energy and Nuclear Physics. lt connects research and educational activities at the University of Kentucky, Eastern Kentucky University and national DOE research laboratories. Its vision is to establish world-class research status dedicated to experimental and theoretical investigations in strong interaction physics. The research provides a forum, facilities, and support for scientists to interact and collaborate in subatomic physics research. The program enables increased student involvement in fundamental physics research through the establishment of graduate fellowships and collaborative work.