Research of dependency and causality in time series

Description:
Dependency and causality analysis are related in the sense that both deal with relationships among variables. The main difference: the observed dependency can be linear or nonlinear, positive or negative but it cannot identify a cause-and-effect relationship, whereas the causality analysis allows to achieve that purpose. The research can be followed to determine the multiple causality, conjectural causality, mutual effect and influence of time factor. The analysis of relationship among macro indicators and stock index could be as an example of the research. Potential customers of this service – business units that see this analysis relevant for the decision making, in forecasting or scenario analysis.

Data analysis

Description:

Data analysis

(FEM) Finite element modeling of physical phenomena

Description:

Finite Element Modeling (FEM) service offers a comprehensive approach to simulate and analyze a wide array of physical phenomena, e.g., optics, nanophotonics, heat transfer, and structural mechanics. Using advanced computational techniques, we break down complex systems into smaller elements. The simple equations that model these finite elements are then assembled into a larger system of equations that models the entire problem allowing conclusions to be drawn about the behavior of complex systems. In structural mechanics, the simulation of mechanical responses within materials and structures helps predict stress distribution, deformation, and failure mechanisms under various loading conditions. For optics and nanophotonics, FEM modeling facilitates the understanding of light propagation, interaction with materials, and the behavior of electromagnetic waves at the nanoscale. In the domain of heat transfer, FEM allows for the precise analysis of thermal behavior within materials and systems. We predict temperature distributions, heat flow, and thermal stresses, aiding in the design of efficient cooling systems.