Finite Element (FE) models provide a rich framework to simulate dynamic biological systems, with applications ranging from hearing to cardiovascular research. With the growing complexity and sophistication of FE bio-simulation models (e.g. multi-scale and multi-domain models), the effort associated with the creation, analysis and reuse of
a FE model can grow unmanageable. This work investigates the role of semantic technologies to improve the automation, interpretation and reproducibility of FE simulations. In particular, the paper focuses on
the definition of a reference semantic architecture for FE bio-simulations and on the discussion of strategies to bridge the gap between numerical-level
and conceptual-level representations. The discussion is grounded on the SIFEM platform, a semantic infrastructure for FE simulations for cochlear mechanics.
A Semantic Web Platform for Automating the Interpretation of Finite Element Bio-simulations
1. A Semantic Web Platform for Automating the Interpretation of Finite Element Bio-simulations
Andre Freitas, Kartik Asooja, Joao B. Jares,
Stefan Decker, Ratnesh Sahay
SWAT4LS 2014
3. Goals
Automate the interpretation of finite element (FE) biosimulations ...
... by providing a supporting a symbolic representation of FE data.
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7. Characteristics of the FE Domain
•Relatively small set of concepts
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8. Characteristics of the FE Domain
•But difficult to represent
•Physics, geometrical models, topological relations, algoithmic, mathematics
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9. Characteristics of the FE Domain
•Most data is at the numeric level
•Highly dependent on visualization (man in the middle)
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10. Dimensions of a FE Bio-simulation
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15. And others ... (which are not covered here)
•Anatomical
•Physiological
•Histological
•...
02 May 2014
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16. Lid-driven cavity flow
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Physical Model
Solver
FEM Model
If there a vortex close to the lid?
17. Lid-driven cavity flow
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Physical Model
Solver
FEM Model
If there a vortex close to the lid?
Informal definition of a valid simulation
18. Numerical Data Interpretation
02 May 2014
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informal description of the simulation
Rules using references to anatomical, physical and data feature elements
Is translated into
Multiple simulations
Feature extraction
Interpretation = rules applied over data at the symbolic level
19. Automatic Interpretation
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Expected physical behavior (Experiment intent):
Velocity in X starts at zero at the bottom of the box followed by a slow velocity decrease reaching a minima which is followed by a very fast velocity increase close to the lid.
Numeric Level
Symbolic Lifting
IF
Predicates
20. Data View
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Data Selection
y
0.05
21. Feature Extraction (Symbolic Lifting)
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Minima=(0.055,-0.20)
fast increase
slow decrease
followed by
(avg first derivative > 35)
velocity starts at 0 at the bottom
maximum velocity is 0.93
at the lid
Based on the TEDDY ontology
23. Data Analysis Rules
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CONSTRUCT
{ :LidSimulation sif: hasInterpretation :ValidVelocityBehaviour }
WHERE {
?dataview rdf:type dao:DataView .
?dataview dao:hasFeature ?x .
...
}
IF( minima(velocity) is negative AND
decreases very slowly(velocity) AND
increases very fast (velocity) )
VALID VELOCITY BEHAVIOUR
SPARQL Rule
35. Take-away message
•Contemporary science demands new infrastructures to scale scientific discovery in a complex knowledge environment.
•Numerical data is everywhere, not only in FE simulations.
•In this work we started exploring how to represent and extract numerical data features to a conceptual level.
•Which could match user intents specified as rules in the data.
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36. Future Directions
•Better integration of the proposed representation and data analysis framework to the TEDDY conceptual model.
•Use of the feature set and rules as a heuristic method to prune the simulation configuration space.
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