Abstract: A new pattern completion method has been applied and tested on a variety of site characterization problems. Applications include developing maps of discrete spatially-distributed fields (e.g. log-hydraulic conductivity fields) given estimates of hydraulic conductivity from pumping tests and classifying soil lithology given soil sample descriptions from driller well logs. This site characterization method SCANN (Site Characterization using Artificial Neural Networks) is based on the application of artificial neural networks, however, it possesses many operational objectives of the kriging methods. Unlike the kriging methods, SCANN is data-driven and requires no estimation of a covariance function. It uses a feed-forward counterpropagation training approach to determine a "best estimate" or map of a discrete spatially-distributed field, given some fixed amount of information. This paper addresses the problem of identifying discrete spatially-distributed fields using both quantitative and qualitative information measured at Lawrence Livermore National Laboratory (LLNL). Two- and three-dimensional maps characterizing the subsurface materials at LLNL are generated and compared using the methods of Ordinary Kriging, Cokriging and SCANN. These realizations are being used to promote greater certainty in the subsurface contaminant transport model used to predict optimal remediation designs at LLNL.
[edit database entry]
Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).