Multiphysics simulations cover a range of scenarios in which multiple models interact in nontrivial ways. In some cases, fundamentally different physical descriptions are coupled across interfaces, as in fluid-structure interaction. In others, alternative models of the same physical process, differing in resolution or fidelity, must be combined within a single simulation, e.g., when combining compressible Euler and magnetohydrodynamics formulations. Yet another class involves the interaction of processes with different characteristic scales or governing equations that coexist within the same physical domain, for example in coupled flow-aeroacoustics or flow-gravity problems. Despite their different physical interpretations, these scenarios pose similar challenges from a computational and algorithmic perspective. The interacting models are typically heterogeneous in their mathematical structure, numerical discretization, and computational requirements, and must be coordinated consistently in space and time. These challenges are further exacerbated by the use of mesh or model adaptivity and when targeting heterogeneous computing architectures. Against this background, this talk discusses the development and implementation of multiphysics coupling techniques for such heterogeneous settings. The presented concepts are illustrated using examples from Trixi.jl and TrixiParticles.jl, two open-source software packages for high-performance simulations in Julia. Trixi.jl is a high-order numerical solver for hyperbolic partial differential equations that enables the exploration of mesh and model adaptivity in grid-based coupled simulations. TrixiParticles.jl is a particle-based solver for multiphysics simulations on complex geometries, with a focus on robustness and versatility. Using selected representative application scenarios, we discuss practical challenges and design choices arising from adaptivity, heterogeneous models, and heterogeneous computing architectures.