Particle-based methods, such as Smoothed Particle Hydrodynamics (SPH), the Discrete Element Method (DEM), and the Lattice Spring Model (LSM), offer a practical framework for simulating systems with interacting physical phenomena. These methods operate on a shared principle: they all simulate systems by exchanging forces between particles, and the nature of these forces—whether pressure and viscous forces for fluids or elastic forces for solids—defines the specific type of physics being modelled. Discrete Multiphysics (DMP) capitalises on this shared framework, allowing different particle methods to coexist in the same domain and interact seamlessly by coupling their respective forces to model complex behaviours such as fluid-structure interaction, phase transitions, and dissolution. DMP also integrates naturally with machine learning, enabling the simulation of systems where the physics is only partially understood and must rely on data. This talk will focus on applications like biomedical and multiphase flows, showing how these techniques tackle real-world challenges in multiphysics modelling.