Event Detail

Event Type: 
Applied Mathematics and Computation Seminar
Friday, November 30, 2018 - 12:00 to 13:00
STAG 160

Speaker Info

White Rabbit R&D LLC, Oregon State University, University of British Columbia, Los Alamos National Laboratory

Water and climate have always been existential drivers of human civilization and today are more relevant than ever as both enablers of progress and wealth and threats to safety and well-being. What can modern data analytics teach us about these things? Geophysical processes are, in general, nonlinear phenomena generated by complex open systems, and some threads in academic research have long recognized the power of advanced mathematical concepts and computational data analytics to illuminate their presence and inner workings. However, mainstream research and practical applications often employ streamlined and, typically, linearized representations and methods. There can be powerful logistical motivations and justifications for doing so, but over the long run, it may also hamstring progress. The increasing accessibility of a rapidly growing and diversifying portfolio of powerful data science techniques can facilitate bridging this gap between theory and practice – provided that data-driven discovery and prediction methods are used in a physics-aware manner, and with an eye to providing demonstrably superior practical solutions. This introductory tour, though very far from being in any sense comprehensive, offers a few examples to illustrate these new developments: using neural networks to detect and understand high-frequency nonlinear dynamics in river flow and to perform operational flood forecasting; using information-theoretic methods to discover and characterize nonlinear climate teleconnections to northern-hemisphere water supply; discovering nonlinear phase transition-like behavior and filtering effects in local-scale hydroclimatology using exploratory nonparametric statistical data analysis; applying complex network theory to understand regional-scale hydrometeorological patterns and optimize environmental monitoring systems; building fuzzy expert systems to integrate nonlinear experiential knowledge and holistically summarize and track environmental conditions; and applying a suite of methods to instrumental and paleoclimatic records to work toward coming to terms with fractal, chaotic, and nonstationary dynamics in water and climate.