The DOE SC program in Advanced Scientific Computing Research(ASCR) hereby announces its interest in research applications to explore potentially high-impact approaches in the development and use of data reduction techniques and algorithms to facilitate more efficient analysis and use of massive data sets produced by observations, experiments and simulation.
SUPPLEMENTARY INFORMATION
Scientific observations, experiments, and simulations are producing data at rates beyond our capacity to store, analyze, stream, and archive the data in raw form. Of necessity, many research groups have already begun reducing the size of their data sets via techniques such as compression, reduced order models, experiment-specific triggers, filtering, and feature extraction. Once reduced in size, transporting, storing, and analyzing the data is still a considerable challenge – a reality that motivates SC’s Integrated Research Infrastructure(IRI)
program[1] and necessitates further innovation in data-reduction methods. These further efforts should continue to increase the level of mathematical rigor in scientific data reduction to ensure that scientifically relevant constraints on quantities of interest are satisfied, that methods can be integrated into scientific workflows, and that methods are implemented in a manner that inspires trust that the desired information is preserved. Moreover, as the scientific community continues to drive innovation in artificial intelligence(AI), important opportunities to apply AI methods to the challenges of scientific data reduction and apply data-reduction techniques to enable scientific AI, continue to present themselves[2-4].
The drivers for data reduction techniques constitute a broad and diverse set of scientific disciplines that cover every aspect of the DOE scientific mission. An incomplete list includes light sources, accelerators, radio astronomy, cosmology, fusion, climate, materials, combustion, the power grid, and genomics, all of which have either observatories, experimental facilities, or simulation needs that produce unwieldy amounts of raw data. ASCR is interested in algorithms, techniques, and workflows that can reduce the volume of such data, and that have the potential to be broadly applied to more than one application. Applicants who submit a pre-application that focuses on a single science application may be discouraged from submitting a full proposal.
Accordingly, a virtual DOE workshop entitled“Data Reduction for Science” was held in January of 2021, resulting in a brochure[5] detailing four priority research directions(PRDs) identified during the workshop. These PRDs are(1) effective algorithms and tools that can be trusted by scientists for accuracy and efficiency,(2) progressive reduction algorithms that enable data to be prioritized for efficient streaming,(3) algorithms which can preserve information in features and quantities of interest with quantified uncertainty, and(4) mapping techniques to new architectures and use cases. For additional background, see[6-9].
The principal focus of this FOA is to support applied mathematics and computer science approaches that address one or more of the identified PRDs. Research proposed may involve methods primarily applicable to high-performance computing, to scientific edge computing, or anywhere scientific data must be collected or processed. Significant innovations will be required in the development of effective paradigms and approaches for realizing the full potential of data reduction for science. Proposed research should not focus only on particular data sets from specific applications, but rather on creating the body of knowledge and understanding that will inform future scientific advances. Consequently, the funding from this FOA is not intended to incrementally extend current research in the area of the proposed project. Rather, the proposed projects must reflect viable strategies toward the potential solution of challenging problems in data reduction for science. It is expected that the proposed projects will significantly benefit from the exploration of innovative ideas or from the development of unconventional approaches. Proposed approaches may include innovative research with one or more key characteristics, such as compression, reduced order models, experiment-specific triggers, filtering, and feature extraction, and may focus on cross-cutting concepts such as artificial intelligence or trust. Preference may be given to pre-applications that include reduction estimates for at least two science applications.
Grant 2) ADVANCEMENTS IN ARTIFICIAL INTELLIGENCE FOR SCIENCE
The DOE SC program in Advanced Scientific Computing Research(ASCR) hereby announces its interest in basic computer science and applied mathematics research in the fundamentals of Artificial Intelligence(AI) for science. Specifically, advancements in this area are sought that can enable the development of:
• Foundation models for computational science;
• Automated scientific workflows and laboratories;
• Scientific programming and scientific-knowledge-management systems;
• Federated and privacy-preserving training for foundation and other AI models for science; and
• Energy-efficient AI algorithms and hardware for science.
The development of new AI techniques applicable to multiple scientific domains can accelerate progress, increase transparency, and open new areas of exploration across the scientific enterprise.
SUPPLEMENTARY INFORMATION
AI is one of the most powerful technologies of our time1 and DOE is at the forefront of research and development in AI technologies for enabling scientific discovery and innovation. Core components of the scientific method remain unchanged: Observation, Hypothesis, Experiments, and Analysis. However, DOE recognizes that abundant sources of data, high-performance computing(HPC) and networking, energy-efficient algorithms, and AI-related technologies can be harnessed to significantly accelerate and expand the impact of scientific research. The breadth of applications spans climate science, cybersecurity and electric grid resilience, biotechnology, microelectronics, disaster response, and beyond. Research to address national priorities will require advances and AI innovations in high-level capabilities such as: monitoring and predicting the onset of real-world anomalies and extreme events; adaptive strategies to control the real-time behavior of complex systems, infrastructure, and processes; approaches for the optimal development and design of physical systems; decision-support for planning, risk, and policy formulation; and tools that synthesize scientific knowledge and accelerate the design, manufacturing, testing, and optimization of new technologies. The focus of ASCR research and development investments is on the underlying approaches for AI-enhanced scientific and engineering capabilities and to significantly transform the scientific method for accelerated discovery and innovation.
Realizing the next generation of AI for science will require innovations in both hardware and algorithms. Future AI-enabled scientific workflows are expected to use Machine Learning(ML) to enhance numerical modeling and data analysis along with technologies that process natural and computer-programming languages. DOE’s exascale supercomputers2 are some of the Nation’s most powerful systems for large-scale AI training and for tasks integrating AI, modeling, simulation, and data analysis. These exascale and future systems complement the vast array of other AI-enabled HPC and edge systems, including automated laboratories and facilities, that will significantly accelerate scientific progress in the coming decades.
DOE’s scientific community has collectively articulated important research directions toward
3 realizing the promise of AI for science and other DOE missions in the recently-released AI For Science, Energy, and Security report[1], building on the preceding AI for Science report[2], and complementing the report on Opportunities and Challenges from Artificial Intelligence and Machine Learning for the Advancement of Science, Technology, and the Office of Science Missions[3]. The research directions highlighted in these reports, and others, appear prominently in the National Artificial Intelligence Research and Development Strategic Plan[4]. This FOA addresses a broad spectrum of research priorities described in these documents that are critical to enabling trustworthy AI for scientific applications advancing human understanding and addressing national needs.
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Grant 1) Data reduction for science:
Grant 2) ADVANCEMENTS IN ARTIFICIAL INTELLIGENCE FOR SCIENCE