Example-Based, Science-Based, and Rule-Based Modeling
> As the human development necessarily started with "learning by examples" (=EBM), the historic development (from Arabia\ Greece over Galilei and Leonardo up to Newton and Hawkins opened a cosmos of sciences and Sciencs-Based Modeling (SBM), which practically enabled and heavily influences today's human life and society.
> EBM mostly got stuck with the restricted capabilities of human brains, who cannot handle more than a maximum of 2 to 5 parameters. SBM however additionally experienced a gigantic enhancement by the development of computers, which led also to some over-done SBMs, like eg calculating the stiffness of a car body by a more than 500.000 parameter FE-model, or employing today's biggest computers 24\7 in weather prediction, just because "we have the computing power". So, practically, Modeling is SBM only in these days.
> Besides, there are also many powerful statistical models in use . But every statistical model in it's kernel is based on some kind of assumptions (= prejudices), which potentially may lead to unknown errors of the results, especially in complex multi-parametric situations. So, a more general EBM technology, free of any prejudices is the better choice, and the examples fed into statistics can be used directly as input for approximate EBMs.
> The same goes for another important know-how source, eg in product and process design, ie Testing. It delivers lots of examples, fitting the design goals more or less, and so are often more or less valued by the test owner. EBM can use all of the results, especially also the non-fitting ones, to bring new insights and new solutions to the problem at hand.
> Last not least, Rule-Based Models (RBMs} can play an important role in KHE projects. Not so much in the shape of Expert-Systems anymore, like proposed some decades ago, but often enough as arbitrary external restrictions in the parameter space, or restrictive relationships between some parameters. But as such, they most often can be easily integrated and fulfilled in an EBM's parameter set and KHB, respectively.
> In this light, EBMs or sometimes networks of EBMs can be seen and used as a very powerful environment to collect, consolidate, integrate, concentrate, USE, and continuously refine the know-how from arbitrary sources (eg SBM, test, RBM, experts, etc pp) in any problem domain and application area, no matter how complex, interdisciplinary, or even scientifically (un-)explored the problem at hand may be.
> As an example, the whole design (and manufacturing) know-how of a car (as a complex today's product) can be collected, stored, integrated, and refined in an appropriately structured network of EBMs, containing the complete and continuously refined know-how of a big team or organization, maintained over the years. Not only for the know-how of a single car, but of a whole range of car types, the car company might be interested in. So, the "development of a new car" can be reduced to a work of minutes (compared to some years today), by employing the appropriate network of EBMs, and something like "customer-specific car design" (and manufacturing) can become reality, if desired.
> So it's more than time to remember EBM, and to emphasize and extend the restricted EBM capabilities of human brains by developing and deploying reliable, problem-oriented networks of EBMs, which can concentrate the know-how from any sources, and can represent and handle today's complex inter-disciplinary problems in a convenient and powerful way.
> Along this line, opportunities can be opened to use the plethoria of examples available in our world, but treated and wasted most often as "trash" up to now. Everything = Examples! Any source can be integrated! Let us start to (re-)use the know-how implicitly available in examples!. Everything done and happening can be understood in this way as (a sequence of) examples to be fed into appropriate EBMs, to gain and to re-use the know-how contained, and to identify and to gain new insights, hidden up to now in the plethoria of examples dumped carelessly.