We address the question of how viscosity impacts the growth of gravitation waves, such as those on the ocean, when they are driven by wind. There is so far no general rigorous theory for this energy transfer. We extend Miles' approach [J. W. Miles, “On the generation of surface waves by shear flows,” J. Fluid Mech. 3, 185–204 (1957)], using the same logarithmic wind profile, to incorporate bulk viscosity and derive modified growth rates. Exploiting the fact that water waves fall into the “weak viscosity” regime, we produce analytical expressions for the growth rate, which we solve using the numerical method proposed by Beji and Nadaoka [“Solution of Rayleigh's instability equation for arbitrary wind profiles,” J. Fluid Mech. 500, 65–73 (2004)]. Our results confirm that corrections to the growth rates are significant for wavelengths below a meter, and for weak to modest wind strengths. We show that all wave growth is suppressed, due to viscous effects, below a critical wind strength. We also show that the wave age corresponding to a developed sea is reduced by viscosity. We quantitatively characterize the zones, in terms of wind strength and wavelength, for which the wave growth is suppressed by viscosity.

Monte Carlo simulations are widely employed to measure the physical properties of glass-forming liquids in thermal equilibrium. Combined with local Monte Carlo moves, the Metropolis algorithm can also be used to simulate the relaxation dynamics, thus offering an efficient alternative to molecular dynamics. Monte Carlo simulations are, however, more versatile because carefully designed Monte Carlo algorithms can more efficiently sample the rugged free energy landscape characteristic of glassy systems. After a brief overview of Monte Carlo studies of glass-formers, we define and implement a series of Monte Carlo algorithms in a three-dimensional model of polydisperse hard spheres. We show that the standard local Metropolis algorithm is the slowest and that implementing collective moves or breaking detailed balance enhances the efficiency of the Monte Carlo sampling. We use time correlation functions to provide a microscopic interpretation of these observations. Seventy years after its invention, the Monte Carlo method remains the most efficient and versatile tool to compute low-temperature properties in supercooled liquids.

Normalizing flows can transform a simple prior probability distribution into a more complex target distribution. Here, we evaluate the ability and efficiency of generative machine learning methods to sample the Boltzmann distribution of an atomistic model for glass-forming liquids. This is a notoriously difficult task, as it amounts to ergodically exploring the complex free energy landscape of a disordered and frustrated many-body system. We optimize a normalizing flow model to successfully transform high-temperature configurations of a dense liquid into low-temperature ones, near the glass transition. We perform a detailed comparative analysis with established enhanced sampling techniques developed in the physics literature to assess and rank the performance of normalizing flows against state-of-the-art algorithms. We demonstrate that machine learning methods are very promising, showing a large speedup over conventional molecular dynamics. Normalizing flows show performances comparable to parallel tempering and population annealing, while still falling far behind the swap Monte Carlo algorithm. Our study highlights the potential of generative machine learning models in scientific computing for complex systems, but also points to some of its current limitations and the need for further improvement.

Soft amorphous materials are viscoelastic solids ubiquitously found around us, from clays and cementitious pastes to emulsions and physical gels encountered in food or biomedical engineering. Under an external deformation, these materials undergo a noteworthy transition from a solid to a liquid state that reshapes the material microstructure. This yielding transition was the main theme of a workshop held from January 9 to 13, 2023 at the Lorentz Center in Leiden. The manuscript presented here offers a critical perspective on the subject, synthesizing insights from the various brainstorming sessions and informal discussions that unfolded during this week of vibrant exchange of ideas. The result of these exchanges takes the form of a series of open questions that represent outstanding experimental, numerical, and theoretical challenges to be tackled in the near future.

We use molecular dynamics simulations to study the structural and mechanical properties of bead-spring polymer networks. In this study we deal with systems which are biologically-relevant as they result from the action of enzymes, i.e. biological catalysts. The latter convert repulsive monomers into attractive ones and hence, starting from a polymer solution, trigger the formation of a physically-crosslinked polymer network. This gel has a remarkably regular mesostructure in the form of a cluster phase. We simulate uniaxial tension of these networks. The evolution of their structural and mechanical properties during deformation is monitored by computing quantities such as the anisotropic pair correlation functions, Poisson's ratio, elastic moduli and stress-strain curves, and the effects of temperature and system composition (i.e. fraction of attractive monomers) are investigated.