Virus-like particles (VLPs) diffuse with tremendous speeds around lung cells to find docking sites. Interferences of partially coherent photons in 100 Hz Rotating Coherent Scattering (ROCS) imaging can make 100 nm small VLPs visible, even behind living cells. In this talk I discuss how incoming photons and amplified photons Ei.Es scattered from virus-like particles f(r,t,w) can be concerted in such a way that their spatio-temporal correlations (coherence) provide high-speed, super-resolution images giving insights into virus-infection or material specific molecular polarizabilities.
Furthermore, I elucidate how the coupling between bacteria-like particles and living cells is investigated by controlling distance and contact with laser traps and MHz interferometric particle tracking. I discuss correlated particle motions, resulting from timescale dependent memory effects in viscoelastic media. Using a frequency decomposition of the tracked particle motions, the apparently invisible binding of particles to the cell can be made visible.
In today’s revolutionary AI development, physics has played a significant role, as it is obvious from the Novel prize in 2024. In this talk, I point out a certain similarity between neural networks and quantum mechanics. Both have almost one century of history, while their intertwining started quite recently.
In the first half of the colloquium, I briefly summarize the reasons why neural networks are strong tools in physics, and introduce the basic AI methods. As a director of MLPhys initiative in Japan, I will report recent results of research unifying AI and theoretical physics in Japan, ranging from lattice QFT and condensed matter physics to automated mathematics.
In the second half of the colloquium, I explain our discovery of the similarity between neural networks and quantum mechanics. I provide a novel map with which a wide class of Euclidean quantum mechanical systems can be cast into the form of a neural network with a statistical summation over network parameters, which brings about a neural network representation of interacting quantum systems / field theories. In this manner, two major fields --- AI and physics --- can be joined together, which hopefully will foster the coming century of sciences.
How fast can ice sheets disintegrate? When do induced earthquakes pose unacceptable risk? Why do volcanoes erupt? And how can we limit the destructive reach of tsunamis? The hidden trigger of what at first glance might seem like disparate systems is a sudden transition from distributed deformation to localized sliding. Independent of whether this transition occurs in ice, rock, magma, or sand it is associated with a sudden shift in the time scale at which the natural system responds at the small scale, causing an abrupt change in the dynamic of the natural system at the large scale. It thus lies at the heart of seemingly distinct natural disasters such as ice-sheet collapse, explosive volcanic eruptions, or injection-induced earthquakes. By developing computational models that are both process-based and testable against observations, I aim to uncover the physics that governs these transitions and translate that knowledge into strategies for reducing risk.
Equilibrium self-assembly and conventional materials processing techniques fall far short of mimicking dynamic self-actuating processes that are commonplace throughout biology. To bridge the gap between living and synthetic matter, we study adhesive non-thermal fibers immersed in an active fluid. Autonomous chaotic flows power non-equilibrium fiber dynamics, inducing their collisions, generating connections, and weaving a membrane-shaped elastic network. This active assembly generates a hierarchy of shapes, structures, and dynamical processes spanning nanometers to centimeters. Ultimately, it generates an active membrane that exhibits global limit cycles induced by a non-reciprocal coupling between the elastic membrane deformations and the alignment axis of the polar active fluid. Our work merges self-assembly with active matter, demonstrating self-processing materials wherein hierarchical life-like structures and dynamics emerge from an initially structureless suspension.
The prize is awarded once every two years for an outstanding PhD thesis in the field of materials science. The main presentation by the prize winner will be in *ENGLISH*.
Warum fällt vielen Studienanfänger*innen der Einstieg in die Physik so schwer – und wie lassen sich ihre Lernprozesse besser verstehen und gezielter unterstützen? Trotz bekannter Hürden in der Studieneingangsphase wird die universitäre Physiklehre in der deutschsprachigen fachdidaktischen Forschung bislang nur vereinzelt untersucht. Der Vortrag stellt ein Forschungsprogramm vor, das diese Lücke systematisch schließt.
Im Mittelpunkt stehen Studien zum Verständnis physikalischer Repräsentationen – etwa von Vektorfeldern und vektoriellen Differentialoperatoren – sowie zu typischen Lernschwierigkeiten im Umgang mit diesen Konzepten. Eye-Tracking-Experimente erlauben dabei Einblicke in visuelle Aufmerksamkeitsmuster, Denkprozesse und Fehlvorstellungen.
Die Ergebnisse fließen in die Entwicklung neuer, vorlesungsbegleitender Aufgabenformate ein, die in der Lehre erprobt und nach dem Prinzip der evidenzbasierten Medizin evaluiert werden.
So entsteht ein enger Forschungs-Lehr-Zirkel, der zeigt: Hochschuldidaktik Physik ist kein Randthema, sondern ein Schlüssel zur Weiterentwicklung universitärer Lehre – vorausgesetzt, sie bekommt den Raum, den sie verdient.