Kolloquien

Unlike artificial neural networks (ANNs), which focus on maximizing accuracy, biological systems excel at handling uncertainty. This ability is believed to be essential for adaptability and efficiency, yet traditional ANNs, implemented on deterministic hardware, struggle with capturing the full probabilistic nature of inference. To address this limitation, Bayesian neural networks (BNNs) replace deterministic parameters with probability distributions, allowing us to distinguish between epistemic uncertainty (due to limited data) and aleatoric uncertainty (arising from noise).   more...

What is the fundamental quantum structure of spacetime? This question constitutes a persistent challenge in physics and several candidate theories of quantum gravity have been developed in response to this challenge. However, experimental tests of these theories are extremely rare, because the typical scale of quantum gravity, the Planck scale, is much smaller than distance scales that can be probed experimentally.   more...

In recent years, novel geometric structures were found both in particle physics and cosmology. This applies to scattering amplitudes, which describe elementary particle collisions, and cosmological correlators, which are relevant for early universe cosmology. So far, the new structures have appeared in various toy models.   more...

High and ultrahigh energy neutrinos provide a unique window into the most extreme environments in the Universe. Produced in powerful cosmic accelerators such as active galactic nuclei, these elusive particles traverse vast cosmic distances and reach Earth unharmed by magnetic fields or matter. In this talk, we explore how neutrinos act as messengers from supermassive black holes, revealing the physical conditions and processes in their vicinity. We also consider how their observation, in combination with other messengers, may provide subtle clues to the intimate nature of gravity at the quantum level.   more...

Among all known isotopes, Thorium-229 has the lowest nuclear excited state, only 8.4 eV above the ground state. This so-called “isomer” is accessible to VUV laser excitation and a multitude of applications at the interface of atomic and nuclear physics have been proposed, including a nuclear clock, a gamma laser and a sensitive detector for variations of fundamental constants.   more...

Ultracold mixtures combine atomic species with fundamentally different properties. Bosons can meet fermions, heavy particles can meet light ones, interparticle interactions can be controlled precisely by external fields, and novel quantum systems emerge with intriguing properties. Over the past 25 years, great progress to prepare various types of mixtures in the laboratory has opened up numerous excited research avenues, related to the few- and many-body physics of atomic and molecular quantum systems.   more...

Isolated systems tend to evolve towards thermal equilibrium, a special state that has been a research focus in physics for more than a century. By contrast, most processes studied in living and life-like systems are driven and far from thermal equilibrium. A fundamental overarching hallmark of all these processes is the emergence of structure, order, and information, and we are facing the major challenge of identifying the underlying physical principles. Two exciting problems are the self-organised formation of spatio-temporal patterns and the robust self-assembly of complex structures.   more...

The quantitative understanding of glacial ice melting into the ocean is one of the most outstanding challenges in environmental fluid dynamics. The lack of understanding is on a fundamental level, due to the highly complex multi-scale, multi-physics nature of the problem. The process involves intricate multi- way coupling effects, including thermal convection, salinity, ocean current, and radiation, etc. As ice melts into the surrounding salty water, a decrease in local salt concentration leads to reduced water density, inducing upward buoyant forces and, consequently, upward flow.   more...

Generative modelling with normalizing flows has worked well in scientific applications like simulation-based inference. However, the peculiar design makes it difficult to incorporate prior knowledge (such as laws of physics or chemistry) into their architecture. Free-form flows eliminate this restriction by means of a new training algorithm. Manifold free-form flows elegantly exploit these opportunities in the case when we know that the data reside on a manifold.   more...

Quantum computers have the potential to solve complex problems efficiently. However, to unleash their full potential, complex quantum systems have to be manufactured, manipulated and measured with unprecedented accuracy and precision.   more...