Carbon Cycle Catastrophes: A Dynamical Systems Perspective

Daniel H. Rothman explains how dynamical systems theory helps in the understanding of disruptions of the carbon cycle in the geological record.

Nearly Three Decades at Snowbird: The Iconic Venue and its Influence on Dynamical Systems at SIAM

Hans Kaper and Marty Golubitsky reflect on the history of Dynamical Systems at Snowbird as the conference moves to Portland in 2021.

Dynamics Days US 2020 Recap

Joseph D. Johnson, PhD Candidate, Engineering Sciences and Applied Mathematics, Northwestern University, reports on Dynamics Days 2020, Hartford, CT, Jan. 3-5, 2020.

2019 SIAM Conference on Analysis of Partial Differential Equations

Stephanie Dodson reports on the 2019 SIAM Conference on Analysis of Partial Differential Equations held December 11th-14th in La Quinta, California, U.S.

Short Amphiphilic Molecules: Curve Lengthening, Defects, and the Role of Cholesterol

Yuan Chen, Andrew Christlieb, Keith Promislow, Frits Veerman, and Sulin Wang report on their research project where they derived a singularly perturbed problem for the cholesterol-loaded structure embedded within the singular scaling of the phospholipid bilayer and showed that the cholesterol sublayer robustly stabilizes the membrane to defect-generating pearling perturbations.

Andrea L. Bertozzi, the 2019 Ralph E. Kleinman Prize recipient

George Em Karniadakis (Brown University) and Mason A. Porter (UCLA) write about Andrea L. Bertozzi as she receives the 2019 Raph E. Kleinman Prize.

Bruno Eckhardt (1960-2019)

Bruno Eckhardt obituary.

Winners of the DSWeb 2019 Contest Tutorials on Dynamical Systems Software

The winners of the DSWeb 2019 Software Tutorial Contest are revealed!

Mathematical Modeling Gains Days for Brain Cancer Patients

For brain tumors such as glioblastoma it is difficult for physicians to access the outcome of a particular course of therapy. Matthew R. Francis reports on a proliferation-invasion model that was constructed by Kristin Rae Swanson and her colleagues at the Mayo Clinic. The model consists of a reaction-diffusion equation to estimate how cancer cells spread throughout the brain and uses magnetic resonance imaging data for calibration. Swanson hopes that use of this model might help to predict patient response to a given regimen and therefore lead to an individualized treatment.