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# Funding Opportunity: DOE Plans to Release 2019 Upcoming Funding Opportunities

#### Future Research Directions for Applied Mathematics, Computer Science, and High Performance Computing

The following funding and research opportunities were released by the U.S. Department of Energy and written by Lewis-Burke Associates.

The analysis below provides advance intelligence on upcoming funding opportunities and future research directions for the Department of Energy Office of Science in applied math, computer science, and high performance computing. The analysis is based on information from the March 26-27 Advanced Scientific Computing Research Advisory Committee (ASCAC) meeting and discussions with DOE program managers. This Advisory Committee provides advice to the Office of Science to advance the research and infrastructure priorities of the Advanced Scientific Computing Research (ASCR) program.

### Upcoming Funding Opportunities

Within the next two months, ASCR plans to release four FY 2019 funding opportunity announcements open to research universities and national laboratories:

Source: DOE.

### Future Research Priorities and FY 2020 Funding Opportunities

The two main research priorities over the next 18 months are:

1. Artificial intelligence and machine learning. The FY 2020 President’s budget request proposes $36 million for artificial intelligence within ASCR, an increase of$23 million or 177 percent above FY 2019. The focus is on addressing basic research needs in scientific machine learning and extremely heterogeneous systems. The major funding opportunities in FY 2020 are likely to be:
• Foundational research in applied mathematics for scientific machine learning. The focus is on improving the reliability, robustness and interpretability of big data and artificial intelligence technologies and start to develop new algorithms, methods, and software tools for extracting information from scientific and engineering data. DOE recently released a report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence, which is available here. The contents of this report will form the basis for DOE’s future investments in scientific machine learning. DOE is particularly interested in using machine learning to derive scientific discovery from the data generated by its user facilities and the exascale computing systems that will soon be deployed. In order to realize this vision, the report identifies six Priority Research Directions which are equally divided between two themes: foundational research, which corresponds to domain awareness, interpretability, and robustness; and capability research, which focuses on data analysis, machine learning-enhanced modeling and simulation, and intelligent and automated decision-making. The table below summarizes future research priorities and directions.
• Co-Design Center for a Distributed Computing Ecosystem. A new Co-Design center that would develop hardware, software, and algorithms needed to integrate big data to support the large-scale computing and data requirements for machine learning.
2. QIS. The FY 2020 President’s budget request proposes $51 million for QIS within ASCR, an increase of$17 million or 50 percent above F 2019. The major funding opportunities in FY 2020 are likely to be:
• Quantum networking. Another funding call to build on FY 2019 awards on quantum networking, as discussed previously.
• Quantum science and technology center. The FY 2020 President’s budget request provides funding for at least one quantum science and technology center authorized by the National Quantum Initiative Act. DOE is hoping that Congress will provide additional funding to support up to five centers, but currently funding is included for only one center. These five-year, multi-disciplinary centers with funding of up to \$25 million a year would focus on addressing scientific grand challenges related to advancing quantum applications in quantum computing, sensing, networking, and communications.

Source: DOE.

With the exascale computing project nearing completion and the end of Moore’s Law quickly approaching, DOE had charged ASCAC with identifying opportunities and challenges for future high performance computing capabilities and recommending areas of research and emerging technologies that need to be given priority in the future. ASCAC released its report on Future High Performance Capabilities on March 20 and it is now available here.

• reconfigurable logic,
• memory-centric processing,
• silicon photonics,
• neuromorphic computing,
• quantum computing, and
• analog computing.

The Advisory Committee emphasized that there will be a period of uncertainty over the next decade on the future trajectory of high performance computing and a number of approaches may be highly disruptive. In addition, the future of computing in the post-exascale and post-Moore eras will be defined by extreme heterogeneity. The challenges and opportunities in an era of extreme heterogeneity are highlighted in a recent report available here. One of the main recommendations to DOE is greater investments in applied math and computer science to be ready for this new era of computing and recruiting, growing, and retaining a future workforce.