Talks

Information flow in self-organized developmental systems

Embryonic development is a spectacular display of self-organization of multi-cellular systems, combining transformations of tissue mechanics and patterns of gene expression. These processes are driven by the ability of cells to communicate through mechanical and chemical signaling, allowing coordination of both collective movement and patterning of cellular states. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understood, we lack principled frameworks to understand how tissues obtain information to generate reproducible patterns. I will discuss how combining dynamical systems models with information theory provides a mathematical language to analyze biological self-organization across diverse systems. Our approach can be used to define and measure the information content of observed patterns, to functionally assess the importance of various patterning mechanisms, and to predict optimal operating regimes of self-organizing systems. I will demonstrate how our framework reveals mechanisms of self-organization of in vitro stem cell systems in direct connection to experimental data, including intestinal organoids and gastruloids. This framework provides an avenue towards unifying the zoo of chemical and mechanical signaling processes that orchestrate embryonic development.

Dynamics from Spectral Theory in Quasiperiodic Systems

The robustness of topological properties, such as quantized currents, generally depends on the existence of gaps surrounding the relevant energy levels or on symmetry-forbidden transitions. In arXiv:2407.07049, we observe quantized currents that survive the addition of bounded local disorder beyond the closing of the relevant instantaneous energy gaps in a driven Aubry–André-Harper chain, a prototypical model of quasiperiodic systems. We explain the robustness using a local picture in configuration-space based on Landau-Zener transitions, which rests on the Anderson localisation of the eigenstates and propose a protocol, realizable in cold atoms or photonic experiments, which leverages this stability to prepare topological many-body states with high Chern numbers. In this talk, I'll explain how to understand this behavior from the language of spectral theory, specifically the asymptotics of Toeplitz operators, and discuss open questions arising from the observation of this topological stability without gaps.

Strong Disorder Renormalization Group Method for Bond Disordered Quantum Spin Chains with Long Range Interactions

We introduce and implement a reformulation of the strong disorder renormalization group (SDRG) method in real space, which is well suited to study bond disordered power law long range coupled quantum spin chains. We apply the method to derive the entanglement entropy growth after a global quench and find at a critical power law exponent a transition from logarithmic to subvolume law growth with time. We trace that transition to the emergence of rainbow states. https://arxiv.org/abs/2501.07298

Microwave studies of complex systems

A review is given on the microwave studies performed in the Marburg quantum chaos group starting from the very beginning about 1990 up to the shut-down two years ago. This includes test of random matrix theory and periodic orbit theory in chaotic microwave resonators, the emission patterns of distorted dielectric resonators, studies of microwave equivalents of graphene-like structures, or the generation of freak waves in a lab size version of the ocean.

Colloquium

Tunable Matter -- Many More is More Different

In 1972 Phil Andersen articulated the motto of condensed matter physics as “More is different.” However, for most many-body systems the behavior of a trillion bodies is nearly the same as that of a thousand. Here I argue for a class of condensed matter, “tunable matter," in which many more is different. The ultimate example of tunable matter is the brain, whose cognitive capabilities increase as size increases from 302 neurons (C. Elegans) to a million neurons (honeybees) to 100 billion neurons (humans). I propose that tunable matter provides a unifying conceptual framework for understanding not only a wide range of systems that perform biological functions, but also physical systems capable of being trained to develop special collective behaviors without using a processor.

Colloquium

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Colloquium SPEQED25

Colloquium