Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Table 5

Mapping between methods/models/techniques and engineering domains.

		General application to engineering	Aerospace	Automotive	Civil engineering	Cyberphysical-systems	Feature engineering	Engineering methods	Industrial applications	Iron mining
General software application			[150]	[89]	[151]			[152]
Artificial intelligence	Deep learning
	Fuzzy logic
	General techniques			[64, 65]	[66, 67]
	Gene Programming							[68]
	Neural networks	[62, 63]						[69]		[70]
	Reasoning techniques					[71]
	Planning			[72]
Computational architecture	Infrastructure	[86–88]
	Scheduling techniques			[89]
	Workflow
Computation model	Event calculus	[96]						[99–101]
	MPI	[97]
	Parallel programming
	Query distribution	[87, 98]
	Stream processing
Computational science	Euler models
	Scientific computation	[104]
	Statistical methods		[105]					[106, 154]
Graph theory	Automata	[124]
	Graph/complex network analysis	[37, 108–117]
Engineering methods	Finite elements	[155]
	Simulation	[96, 120–124]
Machine learning	Bayesian machine learning	[125, 126]
	Data mining
	Information fusion
	Pattern recognition
	Predictive models
	Support vector machines
	Regression model						[127]
Mathematics and applied mathematics	Gradient descent search
	Integer linear programming	[88]
	Linear algebra/solvers	[131, 132]	[137]	[139]
	Linear programming	[136]
	Matrix calculation	[133]
	Nonlinear programming	[62]	[138]							[70]
	Numerical methods							[140]
	Symbolic execution	[134, 135]
Programming techniques	Constraint programming	[74, 145–147]
	Cube computation
	Dynamic programming	[114]
	Domain specific languages	[153]
	Stochastic programming	[122]						[69]	[148]