Computational Modeling Glossary

A simulation composed of autonomous agents that interact according to defined rules, producing emergent system-level behavior.

A finite sequence of well-defined instructions for solving a class of problems or performing a computation.

Constraints imposed on the solution of a differential equation at the boundaries of the problem domain.

The process of adjusting model parameters so that simulation outputs match observed experimental or field data.

The numerical simulation of fluid flow and heat transfer using discretized forms of the governing equations.

The property that a numerical solution approaches the exact solution as discretization parameters are refined.

A model that produces the same output every time for a given set of inputs, containing no random elements.

The process of converting continuous equations and domains into discrete forms suitable for numerical computation.

A fast statistical approximation of a computationally expensive simulation, also called a surrogate model or metamodel.

A numerical scheme where the solution at the next time step is computed directly from known values at the current step.

A numerical technique that approximates derivatives by differences between function values at discrete grid points.

A numerical method that divides a domain into elements and uses variational principles to approximate solutions to PDEs.

A numerical scheme where the solution at the next time step depends on unknown future values, requiring a system of equations to be solved simultaneously.

The state of a system at the starting time of a simulation, providing the values from which the solution evolves.

A discrete representation of the geometry of a problem domain, consisting of nodes and elements for numerical computation.

A computational algorithm that uses repeated random sampling to obtain numerical estimates of quantities.

Simulation approaches that represent phenomena at multiple spatial or temporal scales within an integrated framework.

The property of an algorithm that prevents small perturbations from causing large deviations in the computed solution.

Simultaneous use of multiple processors to solve a computational problem, dividing work to reduce total execution time.

The set of all possible combinations of input parameter values for a given model.

An equation involving partial derivatives of an unknown function with respect to multiple independent variables.

The study of how variations in model inputs propagate to variations in model outputs.

A model that incorporates random variables and produces a distribution of possible outcomes rather than a single deterministic result.

The discrete increment in time used to advance the solution of a time-dependent simulation forward.

The process of determining whether a model accurately represents the real-world system it is intended to simulate.