Gravitational waves promise a new window into the highest-energy events in the evolution of the universe. The recent LIGO/Virgo detections of gravitational waves from the mergers of binary black holes and binary neutron stars and have ignited interest in the future direction of gravitational wave astronomy. A space-based laser interferometer, pioneered by NASA's LISA concept and the European Space Agency's eLISA program and ESA's recent spectacularly successful LISA Pathfinder mission, would enable direct detection of gravitational waves in the milliHertz range. A lower frequency range would allow detection of supermassive black hole mergers, tracing the galaxy merger history and serving as cosmic sirens to probe the universe's expansion history, as well as precursors for the LIGO sources. A space-based detector would also be sensitive to stochastic gravitational wave backgrounds produced by unknown physics operating in the very early universe, including an electroweak phase transition. This Nordita program will bring scientists together to engage in an effort to characterize and detect sources contributing to the gravitational wave background from the early universe, and the implications for new physics at the TeV scale and beyond.

Machine learning has entered the field of quantum matter with applications covering quantum materials and the many-body problem. For example, interpretable and computationally-efficient machine learning models are able to capture the structure-property relationship in materials science. In case of the many-body problem, machine learning architectures provide versatile wavefunctions that lead to accurate results and prove to be more flexible than traditional methods. Conversely, methods in physics have also influenced the development of machine learning methods in the case of tensor networks. The workshop will feature talks by leading experts combined with the talks of younger participants to present a broad picture of the activities and best ideas on the use of ML methods in quantum matter.

In the search for dark matter (DM), one particular focus is on light and ultra-light dark matter, i.e. sub-GeV mass dark matter from a hidden dark sector with a new force interacting with the standard model or ultra-light DM with mass range from 10−22 eV to keV. The arguably most popular example of the latter class is the axion, invoked to solve the apparent absence of CP violation in Quantum Chromo Dynamics. Detection of these particles poses new challenges to potential sensor materials: very small energy depositions, magnetic properties and anisotropic response to particle interactions for example become crucial requirements. The challenge of finding suitable materials fits well with recent developments in solid state physics: Motivated by the exponential growth of computational power and the resulting data, we witness the rapid adoption of functional materials prediction within the framework of materials informatics. Here, methods adapted from computer science based on data-mining and machine learning are applied to identify materials with requested target properties.

The question of how particles and droplets can grow in a turbulent environment is of great current interest in many fields, in astrophysics, cloud microphysics, in biology, and in the engineering sciences. For example, coagulation and condensation in turbulent clouds turn microscopic cloud droplets into rain drops. In astrophysics, planetesimals are thought to form by aggregation of microscopic dust grains in the turbulent environment surrounding a forming star. In both cases, turbulence is believed to be a crucial factor for particle growth. Yet the microscopic mechanisms determining this growth are far from understood. In the past few years there has been substantial progress in understanding the mechanisms that determine how particles move in turbulence, albeit mostly for simplified model systems. The challenge is now to understand how these mechanisms lead to rapid particle growth.