## 2 Discrete Multiphysics  
Different from traditional multiphysics, discrete multiphysics is a mesh-free multiphysics technique based on 'computational particles' rather than on computational meshes [7; 8]. It is a hybrid approach that combines different particle methods such as smoothed particle hydrodynamics (SPH), the lattice-spring model (LSM) and the discrete element method (DEM). The algorithm of particle methods, such as SPH, LSM and DEM, follows the same flowchart (Figure 1a), with the only difference being how the internal forces are calculated. In solid-liquid flows, for instance, there are three types of forces: (i) pressure and viscous forces occurring in the liquid, (ii) elastic forces occurring in the solid and (iii) contact forces occurring when two solids collide with each other. In discrete multiphysics, these forces are achieved by means of three different particle-based methods (Figure 1b): (i) SPH for the liquid, (ii) LSM for the solid and (iii) DEM for the contact forces. Boundary conditions (BC) are also represented by forces: non-compenetration, for instance, is achieved by means of repulsive forces preventing particles overlapping [8]. DMP, therefore, is a metamodel, i.e., a framework for coupling different models within multiphysics simulations. SPH, LSM and DEM are the most common models in DMP, but other choices are possible as long as they follow the flowchart of Figure 1a. Fluctuating hydrodynamics, for instance, could include Brownian dynamics (BD) to account for Brownian fluctuations in the flow.  
Discrete multiphysics has proven to be more than just an alternative to traditional multiphysics. There is a variety of situations where DMP tackles problems that are very difficult, if not impossible, for traditional multiphysics. In traditional multiphysics, domains are assigned during pre-processing: the user establishes, before the simulation, which part of the domain belongs to the solid domain and which part to the fluid domain: this choice cannot change during the simulation. In DMP, the distinction between solid and fluid only depends on the type of force applied to the computational particles and, by changing the type of force, we can change the behaviour of the particles from solid to liquid and vice versa, during the simulation. In the case of solidification, for instance, when the internal energy of a particle goes below a given value, the model used to simulate that particular particle is switched from SPH to LMS. This confers an advantage to discrete multiphysics in a variety of cases (Figure 2) such as cardiovascular flows with blood agglomeration [9; 10; 11], phase transitions [12], capsules and cells breakup [13; 14] and dissolution problems [8; 15].  
Figure 1: (**a**): Typical flow-chart of particle methods; (**b**): Internal forces used in Discrete Multiphysics (DMP).  
Figure 2: Examples of applications of discrete multiphysics: cardiovascular flow (**a**), capsules breakup (**b**), phase transitions (**c**) and dissolution of solid particles (**d**).  
In addition, the particle framework of DMP has proven to be particularly effective when coupled with artificial intelligence (AI) algorithms. In [5], DMP was coupled with reinforcement learning (RL) to account for the effect of the autonomic neural system in multiphysics simulations of human physiology. As a benchmark case, the DMP + RL approach was used for a computer model of the oesophagus with the ability to learn by itself how to coordinate its contractions and propel food in the right direction. In this article, I show that, by establishing particle-neuron duality, DMP can also be effectively coupled with another type of AI algorithm: artificial neural networks.