4.4 Variables and Functions
67
4.4.1 Simulation Variables vs Physical Properties
We open here a parenthesis to discuss the relation between the variables entering a
simulation and the physical properties of real-world objects; this naturally leads to a
comparison of simulations and experiments.
Let’s consider, for example, a certain material (say, liquid water) and focus on its
electric permittivity . The latter is a quantity that appears in electrostatic and electrodynamic laws (e.g. Coulomb’s law), and can be measured experimentally based
on them.
When designing a model for such material that, in particular, captures its electric
permittivity, different approaches are possible: (a) we might take as fixed (a model
parameter, a constant needed as an input), or (b) we could design a model containing
dynamic degrees of freedom that carry electric dipoles, so that is an emergent
property, and the input of the model is instead the properties of the degrees of freedom,
possibly fictitious particles. In both cases, will be matched to the experimental value:
in one case directly, and in the other by tuning the parameters associated with the
simulated entities.
In the case (b), the value of can be estimated using liquid-state theory and
computed from simulations using linear response theory: both procedures are quite
far from what is done experimentally. However, a way to test that the model actually
behaves as it should is to compute the reduction in the force between two fixed ions
due to the presence of the medium; the same test can be done in the case (a), as a
basic check of the numerical implementation of electrostatics, for example.
Now, is this so different from an experiment done on the material? Simulations,
especially those involving some stochastic element, are in many ways similar to
experiments, for example, they also require several repetitions and their results are
affected by statistical errors.
In our view, the concept of observation (which includes measurement) in the
EMMO could be generalized to accommodate also calculations (analytical and
numerical ones), and the variables entering the models (both as input and output)
that are numerical counterparts of physical properties could be recognized as such.
4.5 EngMeta and VIMMP Ontologies
The EngMeta scheme described in Chap. 2 and the VIMMP ontologies presented in
this and in the previous chapter have a relevant overlap in scope, with similar keywords appearing in both assets. From Fig. 2.2, one can see that EngMeta includes
concepts that on VIMMP side are addressed by different ontologies, in particular,
OTRAS (e.g. author, publication, citation), VISO (e.g. software, force-field), VOV
(e.g. variable), MMTO (e.g. project), OSMO (e.g. system component/material) and
VICO (e.g. persons and organizations). The technical metadata (e.g. file size, checksum), instead, are mostly tackled by the Zontal storage itself [35, 36].
67
4.4.1 Simulation Variables vs Physical Properties
We open here a parenthesis to discuss the relation between the variables entering a
simulation and the physical properties of real-world objects; this naturally leads to a
comparison of simulations and experiments.
Let’s consider, for example, a certain material (say, liquid water) and focus on its
electric permittivity . The latter is a quantity that appears in electrostatic and electrodynamic laws (e.g. Coulomb’s law), and can be measured experimentally based
on them.
When designing a model for such material that, in particular, captures its electric
permittivity, different approaches are possible: (a) we might take as fixed (a model
parameter, a constant needed as an input), or (b) we could design a model containing
dynamic degrees of freedom that carry electric dipoles, so that is an emergent
property, and the input of the model is instead the properties of the degrees of freedom,
possibly fictitious particles. In both cases, will be matched to the experimental value:
in one case directly, and in the other by tuning the parameters associated with the
simulated entities.
In the case (b), the value of can be estimated using liquid-state theory and
computed from simulations using linear response theory: both procedures are quite
far from what is done experimentally. However, a way to test that the model actually
behaves as it should is to compute the reduction in the force between two fixed ions
due to the presence of the medium; the same test can be done in the case (a), as a
basic check of the numerical implementation of electrostatics, for example.
Now, is this so different from an experiment done on the material? Simulations,
especially those involving some stochastic element, are in many ways similar to
experiments, for example, they also require several repetitions and their results are
affected by statistical errors.
In our view, the concept of observation (which includes measurement) in the
EMMO could be generalized to accommodate also calculations (analytical and
numerical ones), and the variables entering the models (both as input and output)
that are numerical counterparts of physical properties could be recognized as such.
4.5 EngMeta and VIMMP Ontologies
The EngMeta scheme described in Chap. 2 and the VIMMP ontologies presented in
this and in the previous chapter have a relevant overlap in scope, with similar keywords appearing in both assets. From Fig. 2.2, one can see that EngMeta includes
concepts that on VIMMP side are addressed by different ontologies, in particular,
OTRAS (e.g. author, publication, citation), VISO (e.g. software, force-field), VOV
(e.g. variable), MMTO (e.g. project), OSMO (e.g. system component/material) and
VICO (e.g. persons and organizations). The technical metadata (e.g. file size, checksum), instead, are mostly tackled by the Zontal storage itself [35, 36].