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lowrank committed Aug 21, 2024
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5 changes: 3 additions & 2 deletions Analysis/Awards-Analysis-2024.csv

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17 changes: 9 additions & 8 deletions Applied-Mathematics/Awards-Applied-Mathematics-2024.csv

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"AwardNumber","Title","NSFOrganization","Program(s)","StartDate","LastAmendmentDate","PrincipalInvestigator","State","Organization","AwardInstrument","ProgramManager","EndDate","AwardedAmountToDate","Co-PIName(s)","PIEmailAddress","OrganizationStreet","OrganizationCity","OrganizationState","OrganizationZip","OrganizationPhone","NSFDirectorate","ProgramElementCode(s)","ProgramReferenceCode(s)","ARRAAmount","Abstract"
"2422470","Collaborative Research: NSF-NSERC: Data-enabled Model Order Reduction for 2D Quantum Materials","DMS","COMPUTATIONAL MATHEMATICS, CONDENSED MATTER & MAT THEORY, CDS&E","09/01/2024","08/20/2024","Vikram Gavini","MI","Regents of the University of Michigan - Ann Arbor","Standard Grant","Jodi Mead","08/31/2027","$288,693.00","","vikramg@umich.edu","1109 GEDDES AVE, SUITE 3300","ANN ARBOR","MI","481091079","7347636438","MPS","127100, 176500, 808400","054Z, 079Z, 095Z, 7569, 9216, 9263","$0.00","The project will provide state-of-the-art computational tools for the development of novel 2D materials and their potential application to ultra-fast electronic, opto-electronic, and magnetic devices; unconventional optical and photonic devices; communication devices; and quantum computing applications. The project will address interconnected challenges in emerging areas of quantum science, computational mathematics and computer science by effectively merging highly domain-specific techniques with general machine learning techniques, thus informing and motivating analogous research on model order reduction across the sciences and engineering. 2D materials research is an ideal platform to motivate new mathematics training and curricula in the analysis, modeling, and computation of electronic structure, mechanical and topological properties of materials, and analysis of experimental data. The project?s outreach to female and underrepresented student populations will broaden the diversity of the mathematical research community, and the project provides research training opportunities for graduate students. <br/><br/>Many quantum phenomena of scientific and technological interest emerge naturally at the moir� length scales of layered 2D materials which makes those materials an exciting platform to explore quantum materials properties and to prototype quantum devices. For example, correlated electronic phases such as superconductivity have been recently observed in twisted bilayer graphene (tBLG). Such pioneering results have opened up a new era in the investigation and exploitation of quantum phenomena. Despite the continuing increase in computational resources, high-fidelity modeling and simulation of many quantum materials systems remains out of reach. The limitation is particularly serious in 2D heterostructures due to the large scales at which the quantum phenomena of interest emerge. The objective of this NSF-NSERC Alliance project is to develop an advanced computational modeling workflow, merging state-of-the-art quantum modeling and machine-learning methods to enable rapid, automated, high-fidelity exploration of mechanical and electronic properties of 2D quantum materials. This award is jointly supported by the Division of Mathematical Sciences, the Division of Materials Research and the Office of Advanced Cyberinfrastructure.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria."
"2422469","Collaborative Research: NSF-NSERC: Data-enabled Model Order Reduction for 2D Quantum Materials","DMS","COMPUTATIONAL MATHEMATICS, CONDENSED MATTER & MAT THEORY, CDS&E","09/01/2024","08/20/2024","Mitchell Luskin","MN","University of Minnesota-Twin Cities","Standard Grant","Jodi Mead","08/31/2027","$555,373.00","","luskin@math.umn.edu","200 OAK ST SE","MINNEAPOLIS","MN","554552009","6126245599","MPS","127100, 176500, 808400","054Z, 079Z, 095Z, 7569, 9216, 9263","$0.00","The project will provide state-of-the-art computational tools for the development of novel 2D materials and their potential application to ultra-fast electronic, opto-electronic, and magnetic devices; unconventional optical and photonic devices; communication devices; and quantum computing applications. The project will address interconnected challenges in emerging areas of quantum science, computational mathematics and computer science by effectively merging highly domain-specific techniques with general machine learning techniques, thus informing and motivating analogous research on model order reduction across the sciences and engineering. 2D materials research is an ideal platform to motivate new mathematics training and curricula in the analysis, modeling, and computation of electronic structure, mechanical and topological properties of materials, and analysis of experimental data. The project?s outreach to female and underrepresented student populations will broaden the diversity of the mathematical research community, and the project provides research training opportunities for graduate students. <br/><br/>Many quantum phenomena of scientific and technological interest emerge naturally at the moir� length scales of layered 2D materials which makes those materials an exciting platform to explore quantum materials properties and to prototype quantum devices. For example, correlated electronic phases such as superconductivity have been recently observed in twisted bilayer graphene (tBLG). Such pioneering results have opened up a new era in the investigation and exploitation of quantum phenomena. Despite the continuing increase in computational resources, high-fidelity modeling and simulation of many quantum materials systems remains out of reach. The limitation is particularly serious in 2D heterostructures due to the large scales at which the quantum phenomena of interest emerge. The objective of this NSF-NSERC Alliance project is to develop an advanced computational modeling workflow, merging state-of-the-art quantum modeling and machine-learning methods to enable rapid, automated, high-fidelity exploration of mechanical and electronic properties of 2D quantum materials. This award is jointly supported by the Division of Mathematical Sciences, the Division of Materials Research and the Office of Advanced Cyberinfrastructure.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria."
"2436343","Collaborative Research: MATH-DT: Mathematical Foundations of AI-assisted Digital Twins for High Power Laser Science and Engineering","DMS","OFFICE OF MULTIDISCIPLINARY AC, COMPUTATIONAL MATHEMATICS","10/01/2024","08/09/2024","Andrea Bertozzi","CA","University of California-Los Angeles","Standard Grant","Troy D. Butler","09/30/2027","$569,051.00","Sergio Carbajo","bertozzi@math.ucla.edu","10889 WILSHIRE BLVD STE 700","LOS ANGELES","CA","900244200","3107940102","MPS","125300, 127100","075Z, 079Z, 9263","$0.00","Laser technology is one of the most transformative inventions of the modern era, which has become an indispensable tool for scientific research and technological innovation - revolutionizing the semiconductor industry, telecommunications, healthcare, and defense. However, current laser design and manufacturing approaches remain stagnant, stymieing further breakthroughs. Developing novel integrated systems of laser architectures, components, and techniques leveraging digital twins (DT) is imperative to expand frontiers in intensity, wavelength regime, and high average power. This project will fill this gap using state-of-the-art predictive and generative artificial intelligence (AI) coupled with physical principles and high-fidelity, close-loop, rapid feedback between digital models and physical systems. Graduate students and postdoctoral researchers will also be integrated within the research team as part of the training of the next generation of scientists required to advance the field. <br/><br/>This project will develop theoretical foundations for AI-assisted DTs to integrate scientific data, physical models, and machine learning for complex high-power laser science and engineering (HPLSE) to enable efficient design, failure and performance prediction, operational optimization, and emerging lasing conditions. Laser technologies are extremely complex to model because they rely on a cascaded set of mode-locked laser dynamics and a manifold of architectures and configurations of chirped pulse amplification, and nonlinear optical stages, such as parametric amplification. Their architectural complexity and multi-dimensional data far exceed current modeling and analysis tools. The project will address these challenges by (1) extracting reduced representation of scientific data from experiments or high-fidelity HPLSE simulation, (2) building data-efficient and physics-aware predictive machine learning surrogate models of laser fields with uncertainty quantification, and (3) developing generative model-based rapid closed-loop control between digital models and physical high-power laser systems. The project will be AI-focused, multi-disciplinary, and involve a diverse workforce of future scientists and engineers. The project will also include an education thrust to integrate the research results into interdisciplinary education. The project will bolster AI foundations and its application curricula at both UCLA and the University of Utah. More critically, it will forge a robust collaboration among mathematics, data science, and laser technologies.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria."
"2436319","Collaborative Research: MATH-DT: Mathematical Foundations of Quantum Digital Twins","DMS","OFFICE OF MULTIDISCIPLINARY AC, COMPUTATIONAL MATHEMATICS","09/01/2024","08/09/2024","Daniel Appelo","VA","Virginia Polytechnic Institute and State University","Standard Grant","Jodi Mead","08/31/2027","$299,148.00","Xinwei Deng","appelo@vt.edu","300 TURNER ST NW","BLACKSBURG","VA","240603359","5402315281","MPS","125300, 127100","7203, 9263","$0.00","This project develops, analyzes, and deploys Quantum Digital Twins (QDTs), which are digital clones of existing quantum computers. Built within a comprehensive mathematical and statistical framework, these QDTs will enable bidirectional interactions between quantum computers and virtual models on classical systems, optimizing quantum performance and marking a significant step toward achieving the proverbial Quantum Leap in computational abilities. This advancement will help maintain the United States' leadership in quantum information science and technology, supporting the National Quantum Initiative Act and producing next-generation quantum-enabled technologies for sensing, information processing, communication, security, and computing. Additionally, the project establishes foundations that can enhance other Digital Twin technologies across various fields, from energy to health. It will also facilitate the interdisciplinary training of young scientists in modern data-driven computational methods and the experimental and theoretical aspects of quantum devices and digital twins, with outreach efforts to local communities and Native American tertiary colleges.<br/><br/>The QDTs developed in this project aim to overcome the limitations of traditional quantum simulations, which use a linear component-by-component approach, by introducing four key advancements: (i) the first-ever mathematical formulation of QDTs grounded in a Bayesian probabilistic framework, addressing the inherently probabilistic nature of quantum devices, (ii) new randomized Bayesian experimental design techniques tailored for QDTs, capable of handling the complex dynamics and uncertainties in quantum systems, (iii) a robust generalized Bayesian framework using optimal transportation theory with adaptive prior and model enrichment mechanisms, enabling QDTs to detect and correct their flaws while minimizing system downtime, and (iv) advanced risk-neutral techniques for quantum optimal control and validation, improving QDTs' ability to generate high-fidelity quantum gates. The project also integrates these algorithms and methods into existing open-source software products, demonstrating and disseminating the developed QDTs.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria."
"2436318","Collaborative Research: MATH-DT: Mathematical Foundations of Quantum Digital Twins","DMS","OFFICE OF MULTIDISCIPLINARY AC, COMPUTATIONAL MATHEMATICS","09/01/2024","08/09/2024","Mohammad Motamed","NM","University of New Mexico","Standard Grant","Jodi Mead","08/31/2027","$299,990.00","Gabriel Huerta","motamed@math.unm.edu","1700 LOMAS BLVD NE STE 2200","ALBUQUERQUE","NM","87131","5052774186","MPS","125300, 127100","7203, 7263, 9150, 9263","$0.00","This project develops, analyzes, and deploys Quantum Digital Twins (QDTs), which are digital clones of existing quantum computers. Built within a comprehensive mathematical and statistical framework, these QDTs will enable bidirectional interactions between quantum computers and virtual models on classical systems, optimizing quantum performance and marking a significant step toward achieving the proverbial Quantum Leap in computational abilities. This advancement will help maintain the United States' leadership in quantum information science and technology, supporting the National Quantum Initiative Act and producing next-generation quantum-enabled technologies for sensing, information processing, communication, security, and computing. Additionally, the project establishes foundations that can enhance other Digital Twin technologies across various fields, from energy to health. It will also facilitate the interdisciplinary training of young scientists in modern data-driven computational methods and the experimental and theoretical aspects of quantum devices and digital twins, with outreach efforts to local communities and Native American tertiary colleges.<br/><br/>The QDTs developed in this project aim to overcome the limitations of traditional quantum simulations, which use a linear component-by-component approach, by introducing four key advancements: (i) the first-ever mathematical formulation of QDTs grounded in a Bayesian probabilistic framework, addressing the inherently probabilistic nature of quantum devices, (ii) new randomized Bayesian experimental design techniques tailored for QDTs, capable of handling the complex dynamics and uncertainties in quantum systems, (iii) a robust generalized Bayesian framework using optimal transportation theory with adaptive prior and model enrichment mechanisms, enabling QDTs to detect and correct their flaws while minimizing system downtime, and (iv) advanced risk-neutral techniques for quantum optimal control and validation, improving QDTs' ability to generate high-fidelity quantum gates. The project also integrates these algorithms and methods into existing open-source software products, demonstrating and disseminating the developed QDTs.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria."
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