Mathematical Foundations of Digital Twins (Discontinued)

The Collaboratory to Advance Mathematics Education and Learning (CAMEL) for K-12 initiative aims to advance mathematics learning and education through purposeful collaboration that draws on the interdisciplinary Science of Learning (including neuroscience; cognitive, developmental, and social sciences; computer science; machine learning; engineering; and education research), deep experiences in education practice and teaching, and innovations in the use of data science, AI and technology. Through an agreement with philanthropic partners, including the Walton Family Foundation (WFF), CAMEL consists of two phases. Phase I invites proposals for the creation of new research networks to support the generation of high value datasets that aim to advance math learning and education. These research networks must include researchers who study the basic science of learning, education practitioners, and data scientists. The high value dataset may come from either generating a new dataset or by increasing the value of an existing dataset. Phase II, open only to awardees of Phase I, establishes a “collaboratory,” which is a socio-technological platform that prioritizes community-building and capacity-building to sustain collaborative efforts to advance math learning and education in K-12. Expanding Participation in STEM, NSF Priorities and Gold Standard Science: NSF prioritizes cutting-edge discovery science and engineering research, advancing technology and innovation, and creating opportunities for all Americans. NSF has established priorities set forth by Congress, the administration and the NSF director to promoteNSF's mission. Proposers should review the list ofNSF prioritiesand are encouraged to align their proposals with them, where appropriate. NSF also expects the highest standards of scientific rigor, integrity and adherence to appropriate tenets ofGold Standard Sciencein proposals, as appropriate for the field of science and research modality. Funding Opportunity Number: 26-501. Assistance Listing: 47.075. Funding Instrument: G. Category: ST. Award Amount: $1M – $1.5M per award.

This program will advance scientific community datasets to enable scientific discovery and innovation using artificial intelligence (AI) and other methods. Its goal is to increase the value that can be derived from existing scientific datasets by leveraging novel methods and artificial intelligence (AI). This will unlock new AI-driven insights. It can enable interdisciplinary research. It also enables investigations outside of the original motivation for data collection and analysis. The program seeks to 1) apply AI-based capabilities to feature extraction and metadata generation, and the integration of multiple datasets. 2) Develop robust data pipelines necessary for automated analysis of existing datasets and similar use-cases by AI tools and systems. 3) Augment and/or harmonize existing datasets to better enable use by AI data pipelines and automated analysis. Proposals should address dataset security and integrity. Governance and the process for scientific communities to contribute to the datasets should also be addressed. Proposals are encouraged to leverage existing resources. These may includeNSF data platforms, the NSF Integrated Data Systems and Services program, the NSF-led National AI Research Resource, the Genesis Mission platform, or other national infrastructure. NSF is open to exploring partnerships with philanthropy, private industry, or the non-profit sector to support additional proposals or collaborative opportunitiesthat will advance AI-driven scientific discovery through unlocking the value of high-impact scientific datasets. Expanding Participation in STEM, NSF Priorities, and Gold Standard Science: NSF prioritizes cutting-edge discovery science and engineering research, advancing technology and innovation, and creating opportunities for all Americans. NSF has established priorities set forth by Congress, the administration and the NSF director to promoteNSF's mission. Proposers should review the list ofNSF prioritiesand are encouraged to align their proposals with them, where appropriate. NSF also expects the highest standards of scientific rigor, integrity and adherence tenets ofGoldStandard Sciencein proposals, as appropriate for the field of science and research modality. Funding Opportunity Number: 26-512. Assistance Listing: 47.041,47.049,47.050,47.070,47.074,47.084. Funding Instrument: G. Category: ST. Award Amount: $100M total program funding.

This program will advance scientific community datasets to enable scientific discovery and innovation using artificial intelligence (AI) and other methods. Its goal is to increase the value that can be derived from existing scientific datasets by leveraging novel methods and artificial intelligence (AI). This will unlock new AI-driven insights. It can enable interdisciplinary research. It also enables investigations outside of the original motivation for data collection and analysis.The program seeks to 1) apply AI-based capabilities to feature extraction and metadata generation, and the integration of multiple datasets. 2) Develop robust data pipelines necessary for automated analysis of existing datasets and similar use-cases by AI tools and systems. 3) Augment and/or harmonize existing datasets to better enable use by AI data pipelines and automated analysis. Proposals should address dataset security and integrity. Governance and the process for scientific communities to contribute to the datasets should also be addressed. Proposals are encouraged to leverage existing resources. These may include NSF data platforms, the NSF Integrated Data Systems and Services program, the NSF-led National AI Research Resource, the Genesis Mission platform, or other national infrastructure.NSF is open to exploring partnerships with philanthropy, private industry, or the non-profit sector to support additional proposals or collaborative opportunities that will advance AI-driven scientific discovery through unlocking the value of high-impact scientific datasets.Expanding Participation in STEM, NSF Priorities, and Gold Standard Science:NSF prioritizes cutting-edge discovery science and engineering research, advancing technology and innovation, and creating opportunities for all Americans. NSF has established priorities set forth by Congress, the administration and the NSF director to promote NSF's mission. Proposers should review the list of NSF priorities and are encouraged to align their proposals with them, where appropriate. NSF also expects the highest standards of scientific rigor, integrity and adherence tenets of Gold Standard Science in proposals, as appropriate for the field of science and research modality. Funding Opportunity Number: 26-512. Assistance Listing: 47.041,47.049,47.050,47.070,47.074,47.084. Funding Instrument: G. Category: ST. Award Amount: $100M total program funding.

Mathematical Foundations of Digital Twins (MATH-DT) is sponsored by NSF Directorate for Mathematical and Physical Sciences (MPS) and Air Force Office of Scientific Research (AFOSR). Jointly supports foundational mathematical and statistical research on Digital Twins in applied sciences. The program encourages new collaborative efforts to stimulate fundamental research innovation, push and expand boundaries of knowledge, and explore new frontiers in mathematics and computation for Digital Twin development and applications.

In 2016, the National Science Foundation (NSF) unveiled a set of “Big Ideas,” 10 bold, long-term research and process ideas that identify areas for future investment at the frontiers of science and engineering (see https://www.nsf.gov/news/special_reports/big_ideas/index.jsp). The Big Ideas represent unique opportunities to position our Nation at the cutting edge of global science and engineering leadership by bringing together diverse disciplinary perspectives to support convergence research. As such, when responding to this solicitation, even though proposals must be submitted to the Directorate for STEM Education (EDU) / Division of Equity for Excellence in STEM (EES), once received, the proposals will be managed by a cross-disciplinary team of NSF Program Directors. The INCLUDESInitiative is a comprehensive, national effort to enhance U.S. leadership in science, technology, engineering, and mathematics (STEM) discovery and innovation, focused on NSF’s commitment to ensuring accessibility and inclusivity in STEM fields, as communicated in the NSF Strategic Plan for Fiscal Years (FY) 2022 - 2026. The vision of the INCLUDES Initiative is to catalyze the STEM enterprise to work collaboratively for inclusive change, resulting in a STEM workforce that reflects the Nation’s population. More specifically, the INCLUDES Initiative seeks to motivate and accelerate collaborative infrastructure building to sustain systemic change to broaden participation in STEM fields at scale.Significant advancement of the INCLUDES Initiative's goalswill result in a new generation of STEM talent and leadership to secure the Nation’s future and long-term economic competitiveness. With this solicitation, NSF offers support for five types of projects that connect and contribute to the National Network: (1) Design and Development Launch Pilots, (2) Collaborative Change Consortia, (3) Alliances, (4) Network Connectors, and (5) Conferences. The INCLUDES National Network is a multifaceted collaboration of agencies, organizations, and individuals working collectively to broaden participation in STEM. The INCLUDES National Network serves as a testbed for designing, implementing, studying, refining, and scaling collaborative change modelsand is composed of: INCLUDES funded projects Other NSF funded projects Subcommittee on Federal Coordination in STEM Education (FC-STEM) agencies Scholars engaged in broadening participation research and evaluation, and Organizations that support the development of talent from all sectors of society to build an inclusive STEM workforce. All INCLUDES funded projects must operationalize five design elements of collaborative infrastructure - (1) shared vision, (2) partnerships, (3) goals and metrics, (4) leadership and communication, and (5) expansion, sustainability, and scale- to create systemic change that broadens participation in STEM. Funding Opportunity Number: 22-622. Assistance Listing: 47.041,47.049,47.050,47.070,47.074,47.075,47.076,47.079,47.083,47.084. Funding Instrument: G. Category: ST. Award Amount: $5.5M total program funding.

The NSF FDT-BioTech program, a joint initiative of the National Science Foundation, National Institutes of Health, and the Food and Drug Administration, supports inherently interdisciplinary research projects that underpin the mathematical and engineering foundations behind the development and use of digital twins and synthetic data in biomedical and healthcare applications. The program has a particular focus on digital, in silico models used in the evaluation of medical devices. Projects combine AI and machine learning with mathematical sciences and domain knowledge to develop computational models that can simulate biological systems, predict treatment outcomes, and accelerate medical device evaluation. Each project requires at least two collaborating Senior/Key Personnel with participation from both mathematical sciences and at least one domain knowledge discipline. The program runs annually with a deadline on the first Monday in May.

The NSF STEM K-12 program in the Division of Research on Learning in Formal and Informal Settings (DRL) in the Directorate for STEM Education (EDU) supports fundamental, applied, and translational research that advances STEM teaching and learning and improves understanding of education across the human lifespan and a range of formal and informal settings. Funding Opportunity Number: 25-545. Assistance Listing: 47.076. Funding Instrument: G. Category: ST. Award Amount: $30M total program funding.

The Foundations for Digital Twins as Catalyzers of Biomedical Technological Innovation (FDT-BioTech) program is a tri-agency initiative by NSF, NIH, and FDA supporting inherently interdisciplinary research that underpins the mathematical and engineering foundations behind the development and use of digital twins and synthetic data in biomedical and healthcare applications. The program funds advances in mathematics, statistics, computational sciences, and engineering required to develop responsive digital twin models incorporating artificial intelligence capabilities. Research areas include in silico models for medical device evaluation, synthetic human generation, and emerging challenges in biomedical technology development and assessment. Awards are up to $1,000,000 for collaborative projects from multiple organizations over 3 years, with the program issuing 6 to 10 awards per cycle. The next deadline is May 4, 2026, with annual cycles on the first Monday of May thereafter. This program specifically targets the foundational computational methods that make biomedical digital twins possible rather than application-specific implementations.