Project Grant 2532014

COLLABORATIVE RESEARCH: CPS: MEDIUM: DATA DRIVEN MODELING AND ANALYSIS OF ENERGY CONVERSION SYSTEMS -- MANIFOLD LEARNING AND APPROXIMATION -THIS NSF CPS PROJECT AIMS TO DEVELOP NEW TECHNIQUES FOR MODELING CYBER-PHYSICAL SYSTEMS THAT WILL ADDRESS FUNDAMENTAL CHALLENGES ASSOCIATED WITH SCALE AND COMPLEXITY IN MODERN ENGINEERING. THE PROJECT WILL TRANSFORM HUMAN INTERACTION WITH COMPLEX CYBER-PHYSICAL AND ENGINEERED SYSTEMS, INCLUDING CRITICAL INFRASTRUCTURE SUCH AS INTERCONNECTED ENERGY NETWORKS. THIS WILL BE ACHIEVED THROUGH A NOVEL COMBINATION OF DATA-DRIVEN TECHNIQUES AND PHYSICS-BASED APPROACHES TO GIVE MATHEMATICAL AND COMPUTATIONAL MODELS THAT ARE AT ONCE ABSTRACT ENOUGH TO BE UNDERSTOOD BY HUMANS MAKING KEY ENGINEERING DECISIONS AND PRECISE ENOUGH TO MAKE QUANTITATIVE PREDICTIONS. THE INTELLECTUAL MERITS OF THE PROJECT INCLUDE A NOVEL CONFLUENCE OF EMERGING DATA SCIENCE AND MODEL-ANALYSIS METHODS, INCLUDING MANIFOLD LEARNING AND INFORMATION GEOMETRY. THE BROADER IMPACTS OF THE PROJECT INCLUDE THE TRAINING OF UNDERGRADUATES, INCLUDING THOSE FROM UNDERREPRESENTED COMMUNITIES, SEVERAL OUTREACH ACTIVITIES, AND PUBLICLY AVAILABLE OPEN-SOURCE SOFTWARE. ENGINEERING REQUIREMENTS OFTEN MAKE INCOMPATIBLE DEMANDS ON MODELS. DETAILED MODELS MAKE HIGHLY ACCURATE PREDICTIONS, BUT COARSE MODELS ARE EASIER TO INTERPRET. THIS PROJECT WILL DEVELOP TECHNIQUES TO OVERCOME THIS INHERENT CONTRADICTION. ON THE ONE HAND, DATA SCIENCE AND MACHINE LEARNING TECHNIQUES ALLOW US TO EFFICIENTLY CONSTRUCT BLACK BOX PREDICTIVE MODELS WITH LIMITED GENERALIZABILITY. AT THE SAME TIME, RECENT ADVANCES IN INFORMATION GEOMETRY HAVE PRODUCED MODEL REDUCTION METHODS THAT SYSTEMATICALLY DERIVE SIMPLE, INTERPRETABLE MODELS FROM PHYSICAL FIRST PRINCIPLES THAT SUMMARIZE RELEVANT MECHANISMS NEEDED FOR MODEL TRANSFERABILITY. COMBINING THESE TECHNOLOGIES WILL ENABLE USEFUL MAPPINGS BETWEEN ?PHYSICALLY EXPLAINABLE? REDUCED MODELS AND QUANTITATIVE DATA. THESE DATA-DRIVEN TOOLS WILL ENABLE ?THE BEST OF BOTH WORLDS? ? PHYSICALLY INTERPRETABLE MODELS THAT MAKE QUANTITATIVE PREDICTIONS. WE WILL COMBINE A MEANINGFUL, QUALITATIVELY CORRECT BUT QUANTITATIVELY INACCURATE REDUCED MODEL WITH A DATA-DRIVEN TRANSFORMATION. THE PROJECT TEAM BRINGS TOGETHER DOMAIN-SPECIFIC EXPERTISE IN PHYSICAL MODELING, ENERGY SYSTEMS, AND DATA-DRIVEN LEARNING. WE WILL APPLY THIS APPROACH TO ADDRESS KEY OPERATIONAL CHALLENGES IN INTERCONNECTED ENERGY NETWORKS. THE ENABLING TECHNOLOGY WILL APPLY TO MODELING ANY COMPLEX CYBER-PHYSICAL SYSTEM. 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.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.