from pyKriging.samplingplan import samplingplan # The Kriging model starts by defining a sampling plan, we use an optimal Latin Hypercube here sp = samplingplan(2) X = sp.optimallhc(20) # Next, we define the problem we would like to solve testfun = pyKriging.testfunctions().branin y =...
Experimental design involves not only the selection of suitable independent, dependent, and control variables, but planning the delivery of the experiment under statistically optimal conditions given the constraints of available resources. There are multiple approaches for determining the set of design point...
3.3. Optimal Control eleurent/phd-bibliography: References on Optimal Control, Reinforcement Learning and Motion Planning mintOC Julia: Jump + InfiniteOpt Jump is powerfull!!! jump-dev/JuMP.jl: Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear) Inf...
Latin hypercube Sampler: designed to maximized the information with as few sample as possible, without being deterministic. This is often an optimal choice if you know how much run you will need, but not well suited for incremental exploration (run some samples, explore the results, run other ...
Monte Carlo / Latin Hypercubestochastic power flow based on the input profiles. Blackout cascadingin simulation and step by step mode. Three-phaseshort circuit. Includes the Z-I-P load model, this means that the power flows can handle both power and current. ...
rlabbe/filterpy: Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'. ting...