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 is sponsored by National Science Foundation (NSF) and Air Force Office of Scientific Research (AFOSR). This program jointly supports foundational mathematical and statistical research on Digital Twins in applied sciences. It encourages new collaborative efforts to stimulate fundamental research innovation, expand knowledge boundaries, and explore new frontiers in mathematics and computation for Digital Twin development and its applications.

The U.S. National Science Foundation Directorate for Mathematical and Physical Sciences (NSF MPS) supports multiple research programs at the frontiers of discovery in theoretical and applied mathematical sciences.Programs include:Algebra and Number TheoryAnalysisApplied MathematicsCombinatoricsComputational MathematicsFoundationsGeometric AnalysisMathematical BiologyProbabilityStatisticsTopologyMathematical Sciences Research InfrastructureMathematical Sciences Focused Research GroupsMathematical Sciences Research Training Groups NSF anticipates a portfolio of awards for these programs with a range of budgets, durations and scopes. Program budgets, number of awards, award sizes and durations are subject to the availability of funds.NSF MPS is particularly interested in intellectually ambitious and potentially transformative foundational research. Proposals need not promise a near-term product, deployment, or commercial outcome, nor need they address later-stage development to be competitive. Funding Opportunity Number: 26-520. Assistance Listing: 47.049. Funding Instrument: G. Category: ST. Award Amount: Starting at $300K per award.

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.

Machine Learning and Artificial Intelligence (AI) are enabling extraordinary scientific breakthroughs in fields ranging from protein folding, natural language processing, drug synthesis, and recommender systems to the discovery of novel engineering materials and products. These achievements lie at the confluence of mathematics, statistics, engineering and computer science, yet a clear explanation of the remarkable power and also the limitations of such AI systems has eluded scientists from all disciplines. Critical foundational gaps remain that, if not properly addressed, will soon limit advances in machine learning, curbing progress in artificial intelligence. It appears increasingly unlikely that these critical gaps can be surmounted with increased computational power and experimentation alone. Deeper mathematical understanding is essential to ensuring that AI can be harnessed to meet the future needs of society and enable broad scientific discovery, while forestalling the unintended consequences of a disruptive technology. The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE) will jointly sponsor research collaborations consisting of mathematicians, statisticians, computer scientists, engineers, and social and behavioral scientists focused on the mathematical and theoretical foundations of AI. Research activities should focus on the most challenging mathematical and theoretical questions aimed at understanding the capabilities, limitations, and emerging properties of AI methods as well as the development of novel, and mathematically grounded, design and analysis principles for the current and next generation of AI approaches. Specific research goals include: establishing a fundamental mathematical understanding of thefactors determining the capabilities and limitations of current and emerging generations of AI systems, including, but not limited to, foundation models, generative models, deep learning, statistical learning, federated learning, and other evolving paradigms; the development of mathematically grounded design and analysis principles for the current and next generations of AI systems; rigorous approaches for characterizing and validating machine learning algorithms and their predictions; research enabling provably reliable, translational, general-purpose AI systems and algorithms; encouragement of new collaborations in this interdisciplinary research community and between institutions. The overall goal is to establish innovative and principled design and analysis approaches for AI technology using creative yet theoretically grounded mathematical and statistical frameworks, yielding explainable and interpretable models that can enable sustainable, socially responsible, and trustworthy AI. Funding Opportunity Number: 24-569. Assistance Listing: 47.041,47.049,47.070,47.075. Funding Instrument: G. Category: ST. Award Amount: $500K – $1.5M per award.

The Integrated Data Systems and Services (IDSS) program supports operations-level national-scale cyberinfrastructure systems and services that broadly advance and facilitate open, data-intensive and artificial intelligence-driven science and engineering research, innovation, and education. Through this solicitation, the IDSS program is accepting proposals for three categories of projects: Category I. Development, deployment, and operation of novel national-scale integrated data systems and services, which may include interfacing with or leveraging other existing capabilities, systems and services, as appropriate to the project; Category II.  Transition of established smaller scale, regional, pilot, or prototype data-focused systems and services to national-scale production/operational quality/level. This may also include enhancement and expansion of existing national-scale data-focused operational systems and services; and Category III. Planning grants for future potential development/deployment or transition/enhancement IDSS projects. NSF and the Office of Advanced Cyberinfrastructure (OAC) have long supported the development of innovative foundational and application-specific cyberinfrastructure resources and systems to address data-intensive research needs at the campus, regional, and community scales, through programs such as Cyberinfrastructure for Sustained Scientific Innovation (CSSI), Campus Cyberinfrastructure (CC*), and other investments. The primary goal of the IDSS program is to support national-scale foundational data cyberinfrastructure that broadly enables data- and artificial intelligence-driven research for many communities. The IDSS program supports foundational transdisciplinary and demonstrably multi-disciplinary projects aimed to broadly impact the science and engineering research and education community. Projects that aim to primarily benefit a single science discipline, domain, project, or application are not supported. It is recommended that prospective PIs contact program officer(s) from the list of Cognizant Program Officers to gain insight about alignment of their project ideas with the priorities of the IDSS program and Office of Advanced Cyberinfrastructure. As part of contacting Cognizant Program Officers, prospective PIs are also encouraged to ascertain that the focus and budget of their proposed work are appropriate for this solicitation. Funding Opportunity Number: 26-509. Assistance Listing: 47.070. Funding Instrument: G. Category: ST. Award Amount: $500K – $30M per award.

The NSF FDT-BioTech program (NSF 24-561) supports interdisciplinary research at the intersection of AI, computational modeling, and biomedical innovation by funding the mathematical and engineering foundations behind digital twins and synthetic data for healthcare applications. Digital twins — computational replicas of biological systems, patients, or medical devices — require advanced AI and machine learning methods for their development, calibration, and deployment. The program funds research on methods and algorithms relevant to digital twins and synthetic humans, including AI-driven in silico evaluation of medical devices and treatments. Projects must be inherently interdisciplinary, combining expertise in mathematics, engineering, computer science, and biomedical domains. Collaborative projects across multiple organizations are encouraged and can receive up to $1 million in total funding over up to 3 years. The program is administered by multiple NSF directorates including the Division of Mathematical Sciences and the Office of Advanced Cyberinfrastructure, reflecting its cross-cutting nature. The deadline recurs annually on the first Monday in May.

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.