GCRI · Systems Science and Modelling
STEM foundation of Systems Science and Modelling
The science, technology, engineering and mathematics Systems Science and Modelling rests on. Each method has a canonical page in the STEM Commons, hosted by the Global Centre for Risk and Innovation (GCRI), and a page here on how it is applied in this field.
STEM foundation of Systems Science and Modelling in four parts.
The methods behind the field.
- 01Systems engineeringRequirements for decision models · Interface control for coupled modelsSteward · Systems Science and ModellingApplied here
- 02Probability and statisticsBayesian calibration · Proper scoring of predictions · Ensemble statisticsSteward · Systems Science and ModellingApplied here
- 03Uncertainty quantification and model validationUncertainty budgets · Surrogate models for expensive simulations · Credibility assessmentSteward · Systems Science and ModellingApplied here
- 04Network science and contagionCommon network representations · Contagion model comparison · Multilayer analysisSteward · Systems Science and ModellingApplied here
- 05Complex systems and agent-based modellingAgent-based model standards · Feedback and tipping point analysis · Early warning indicatorsSteward · Systems Science and ModellingApplied here
- 06Optimisation and operations researchProblem formulation guidance · Robust and stochastic comparisons · Solver benchmarkingSteward · Systems Science and ModellingApplied here
- 07Decision analysis under uncertaintyFraming the decision before the model · Value of information for modelsSteward · Systemic Risk ManagementApplied here
- 08High-performance, cloud and edge computingEnsemble design within budgets · Workflow orchestration · Reproducible environmentsSteward · Data and ComputeApplied here
Why systems modelling needs this combination of methods.
Systems modelling combines systems engineering, probability, uncertainty quantification, network science, complex systems, optimisation, decision analysis and computing. The test of any combination is whether its coupled output can be validated against something observed, which is why uncertainty quantification sits at the centre.
Systems modelling combines systems engineering, probability, uncertainty quantification, network science, complex systems, optimisation, decision analysis and computing.
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Pages in STEM foundation of Systems Science and Modelling.
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