Exploring Symmetry in Chaos

Exploring Symmetry in Chaos

Michael Field and Martin Golubitsky announce the second edition of their book Symmetry in Chaos: A Search for Pattern in Mathematics, Art, and Nature.

A Moving Argument

A Moving Argument

Mark Levi talks about stability of a cube resting on a sphere.

A Measure of Morphodynamics

A Measure of Morphodynamics

Mattia Serra and L. Mahadevan talk about understanding the way in which a complex, multicellular organism arises from a single cell via spatiotemporal patterns that are repeatable and reproducible across the tree of life.

Long-time Behavior of Nonlinear Schrödinger Equation Waves

Long-time Behavior of Nonlinear Schrödinger Equation Waves

Katelyn Plaisier Leisman talks about long-time behavior of Nonlinear Schrödinger equation waves.


Intrinsic Criteria for the Stability of Traveling Waves

Intrinsic Criteria for the Stability of Traveling Waves

Paul Cornwell from the Johns Hopkins University Applied Physics Lab (paul.cornwell (at) jhuapl.edu) describes how intrinsic features of travelling waves can be used, along with the Maslov index and the Evans function, to determine features of the associated linear stability problem.

Renewing SIAM membership

Renewing SIAM membership

Renew your SIAM membership.

An Introduction to the Kepler-Heisenberg Problem

An Introduction to the Kepler-Heisenberg Problem

Corey Shanbrom from California State University, Sacramento, CA (corey.shanbrom (at) csus.edu) presents an overview of the Kepler-Heisenberg problem.

Neumann Domains of Equilibrium States

Neumann Domains of Equilibrium States

Graham Cox from the Department of Mathematics and Statistics, Memorial University, St. John's, Canada describes the interplay between dynamical systems and geometry that arises in the study of Neumann boundary value problems.

New Bridges Between Deep Learning and Partial Differential Equations

New Bridges Between Deep Learning and Partial Differential Equations

Lars Ruthotto describes the interplay and exchange of knowledge between deep neural networks and partial differential equations.

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