CMX is a research group aimed at the development and analysis of novel algorithmic ideas underlying emerging applications in the physical, biological, social and information sciences. We are distinguished by a shared value system built on the development of foundational mathematical understanding, and the deployment of this understanding to impact on emerging key scientific and technological challenges.
FacultyVenkat ChandrasekaranMathieu Desbrun Thomas Hou Houman Owhadi Peter Schröder Andrew Stuart Joel Tropp Von Karman
Franca Hoffmann |
Lunch Seminars(Will be held at 12 noon in Annenberg 213, unless otherwise specified.)
November 28, 2018
Mathieu Desbrun ▦ topic tba ▦
January 16, 2109
topic tba ▦ ▦
January 23, 2019
topic tba ▦ ▦
January 30, 2019
Oscar Bruno ▦ topic tba ▦
February 20, 2019
topic tba ▦ ▦
February 27, 2019
Tapio Schneider ▦ topic tba ▦ Other Events
November 15, 2018
• CMX Special Seminar • Annenberg 314 4:00pm Sam Stechmann ▦ Multi-model Communication and Data Assimilation for Mitigating Model Error and Improving Forecasts ▦ Models for weather and climate prediction are complex, and each model typically has at least a small number of phenomena that are poorly represented, such as perhaps the Madden–Julian Oscillation (MJO) or El Nin ~o–Southern Oscillation (ENSO) or sea ice. Furthermore, it is often a very challenging task to modify and improve a complex model without creating new deficiencies. On the other hand, it is sometimes possible to design a low-dimensional model for a particular phenomenon, such as the MJO or ENSO, with significant skill, although the model may not represent the dynamics of the full weather– climate system. Here a strategy is proposed to mitigate these model errors by taking advantage of each model’s strengths. The strategy involves inter-model data assimilation, during a forecast simulation, whereby models can exchange information in order to obtain more faithful representations of the full weather–climate system. As an initial investigation, the method is examined using a simplified scenario of linear models, involving a system of stochastic partial differential equations (SPDEs) as an imperfect tropical climate model and stochastic differential equations (SDEs) as a low-dimensional model for the MJO. It is shown that the MJO prediction skill of the imperfect climate model can be enhanced to equal the predictive skill of the low-dimensional model. Such an approach could provide a route to improving global model forecasts in a minimally invasive way, with modifications to the prediction system but without modifying the complex global physical model itself. The methods may also be applicable to other settings where multiple models can be used together to improve predictions.
November 29, 2018
• CMX Special Seminar • Annenberg 213 12:00pm Hau-Tieng Wu ▦ topic TBA ▦
December 13, 2018
• CMX Special Seminar • Annenberg 213 12:00pm Lisa Maria Kreusser ▦ An Anisotropic Interaction Model for Simulating Fingerprints ▦ Motivated by the formation of fingerprint patterns we consider a class of interaction models with short-range repulsive, long-range attractive forces whose orientations depend on an underlying stress field. This stress field introduces an anisotropy leading to complex patterns which do not occur in the associated isotropic models. The transition between the isotropic and the anisotropic model can be characterized by one of the model parameters and we study the variation of this parameter both analytically and numerically. We analyze the steady states and their stability by considering the particle model and the associated mean-field equations. Besides, we propose a bio-inspired model to simulate fingerprint patterns as stationary solutions by choosing the underlying tensor field appropriately. |
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