Gunhild Elisabeth Berget

SINTEF, Norwegian University of Science and Technology

Papers

Total Citations

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About

Gunhild Elisabeth Berget is a researcher at the forefront of marine robotics and environmental monitoring, specializing in the intersection of autonomous underwater vehicles (AUVs) and stochastic modeling. Her key research areas include adaptive sampling, advection-diffusion dynamics, and real-time environmental data assimilation. Berget’s major contributions lie in developing dynamic stochastic frameworks that enable AUVs to intelligently select sampling sites, optimizing the detection of pollution plumes and ecological disturbances in coastal oceans. Her most-cited work, "Dynamic stochastic modeling for adaptive sampling of environmental variables using an AUV" (2023, 11 citations), demonstrates how integrating ocean models with robotic autonomy can efficiently monitor mine tailings dispersion, protecting sensitive marine areas. Another influential paper (2021, 9 citations) advances onboard stochastic advection-diffusion models for mapping excursion sets, embedding realistic statistical methodologies into robotic platforms. Her 2022 study on adaptive underwater robotic sampling of dispersal dynamics (4 citations) further solidifies her impact. Berget’s work is notable for bridging theoretical statistics with practical oceanography, offering scalable solutions for environmental stewardship. Her achievements highlight a commitment to transforming how we monitor and respond to anthropogenic impacts on marine ecosystems, making her a rising voice in autonomous environmental sensing.