Nonlinear Dynamics and Data: Prediction, State Estimation, and Uncertainty Quantification in Complex Systems
Friday, March 15 - Sunday, March 17, 2019
Warren Weaver Hall (Directions)
Courant Institute of Mathematical Sciences
Modeling complex dynamical system is ubiquitous in many areas of sciences, including climate, geophysics, engineering, neuroscience and material science. The long-standing challenge in modeling dynamical systems is to develop models that are consistent with the available observables yet able to predict the behavior of the quantities of interest. This scientific problem has significant science and society impacts on many areas of sciences, including climate, geophysics, engineering, neuroscience and material science. This extremely difficult task is partly due to the complexity of the underlying dynamical systems, high dimensionality, strong nonlinearity, multiscale structures, non-Gaussian statistics and large uncertainties.
This birthday conference is to honor Andrew Majda who has made significant contributions in this area in the last quarter of the century. His major achievements include an understanding of real-world complex phenomena such as MJO, Monsoon dynamics, and the development of relevant statistical tools such as reduced-order modeling paradigm, data assimilation, uncertainty quantification. This conference will be held at Courant Institute of Mathematical Sciences, New York University on March 15--17, 2019. It will focus on new ideas and branches of scientific disciplines stimulated by his work, including advanced techniques in modeling, data assimilation, prediction and uncertainty quantification. Rigorous math theories, effective numerical algorithms and real-world applications will all be emphasized in this conference.