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Dynamics AI

Michael P. Brenner

AI Institute: Dynamics AI

Research Expertise: theoretical modeling in the physical sciences and engineering. Much of the work has arisen out of close contact with experimentalists, and efforts to develop quantitative descriptions of phenomena and experiments. Problems addressed have included the breaking of fluid droplets; sonoluminescence (the production of light from small bubbles); the sedimentation of small particles; device design in engineering; electrospinning (a materials technique for producing small fibers), etc. Current research directions range from figuring out the nature of the turbulent cascade, to understanding the rules for building materials that assemble themselves, possibly with life-like properties, to efforts to use recent advances in machine learning to facilitate scientific discovery.

AI Institute Project Involvement: Control; Broadening Participation Committee


Dynamics AI

Krithika Manohar

AI Institute: Dynamics AI

Research Expertise: developing algorithms for data-driven prediction and control of complex dynamical systems. Her work uses dimensionality reduction techniques rooted in operator theory and manifold learning to discover physically meaningful features from data, and optimize sensors and actuators for downstream decision-making (sparse sensing). Target applications of sparse sensing optimization include fluid flow reconstruction, control, image recovery, and aircraft manufacturing. She is also interested in sparse sensing and forecasting in the context of partially observed multiscale systems, which commonly occur in fluid dynamics, materials science, and biology.

AI Institute Project Involvement: Sensing & Optimization; Broadening Participation Committee


AI-LEAF

David Mulla

AI Institute: AI-LEAF

Research Expertise: Dr. Mulla’s research emphasizes (1) non-point source surface water pollution and TMDLs and watershed management, (2) the transport and modeling of water, solutes, trace metals, and organic chemicals in soil, surface and ground water, (3) impacts of biofuel and alternative crop production systems, (4) the measurement, modeling and management of soil erosion, (5) phosphorus and nitrogen transport in soils, (6) agricultural best management practices , (7) soil, landscape and terrain modeling for precision conservation, and (9) field-scale variability for precision farming.

AI Institute Project Involvement: Project Group 1: Greenhouse Gas Flux Estimation
Project Group 3: Multi-Criteria Optimization of Mitigation Practices
Project Group 5: Multi-Scale, Multi-Criteria DSS for GHG and Ecosystem Services

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