A categorization of these constitutive relations allows further categorization ofthe nodes and simpler and more generic labeling. While still making no relation toport-based modeling, these categories will be quite general and when the connectionto port-based modeling is made further restrictions can be made, highly depend-ing on the modeling level though, as will be explained when discussing modelingitself. All categories allow the presence of an arbitrary number of outputs (meaningnothing else than making a variable available as independent variable at an inputof another multiport). An arbitrary number of inputs is also allowed: this has beenintroduced already as ‘modulation’ (modulated multiport) and each signal is a mod-ulation signal or modulating signal. Note that one can also consider a multiport asbeing a node with an arbitrary number of inputs and an arbitrary number of outputs1 Concept-Oriented Modeling of Dynamic Behavior 11of which a number (n) of inputs are conjugated (paired) tonoutputs while leavingthe question open which of the variables in a pair gets the role of input or output aslong as the relation is bilateral, i.e., one input and one output. These bilateral pairsare represented by the (power) ports and the other inputs and outputs by the signalports.Earlier we noted that in a design context the constitutive relation can bechosen as the unknown, requiring both effort and flow as inputs, which vio-lates the above use of the causal stroke. This refers to the situation that whena given model structure is found, the equations can be rewritten in such a waythat the constitutive relation, mostly the constitutive parameter, can be foundfor a certain desired behavior. The mathematical solution of this kind of ques-tion is sometimes mapped on the bond graph by using half causal strokes atboth sides of a bond. This graphical approach is addressed by the terminology‘bi-causality’ and is discussed in more detail inChapters 5and6. However, sinceit is not clear what the graphical representation adds to the insight of the modeler –on the contrary, it highly confuses many bond graph novices – we will not discussbi-causality herein any further.The above defines the principles of a bond graph even though it does not becomevery useful without a classification of the node types. However, this classification isin part a matter of taste: The more limited a set of nodes is classified, the more pow-erful conclusions may be drawn from a bond graph, but this also limits the possiblemodels that can be represented at the same time. The most common generic clas-sification is given in the next section. This classification results in part