Library Functions

Structure definition for a NeuralFMU, that runs in mode Co-Simulation (CS).

Structure definition for a NeuralFMU, that runs in mode Model Exchange (ME).

The mutable struct representing an abstract (simulation mode unknown) NeuralFMU.

DEPRECATED:

Wrapper. Call fmi2DoStepCS for more information.

DEPRECATED:

Performs something similar to fmiDoStep for ME-FMUs (note, that fmiDoStep is for CS-FMUs only). Event handling (state- and time-events) is supported. If you don't want events to be handled, you can disable event-handling for the NeuralFMU nfmu with the attribute eventHandling = false.

Optional, additional FMU-values can be set via keyword arguments setValueReferences and setValues. Optional, additional FMU-values can be retrieved by keyword argument getValueReferences.

Function takes the current system state array ("x") and returns an array with state derivatives ("x dot") and optionally the FMU-values for getValueReferences. Setting the FMU time via argument t is optional, if not set, the current time of the ODE solver around the NeuralFMU is used.

DEPRECATED:

fmi2InputDoStepCSOutput(comp::FMU2Component, 
                        dt::Real, 
                        u::Array{<:Real})

Sets all FMU inputs to u, performs a ´´´fmi2DoStep´´´ and returns all FMU outputs.

DEPRECATED:

Wrapper. Call fmi2EvaluateME for more information.

DEPRECATED:

Wrapper. Call fmi2InputDoStepCSOutput for more information.

Compares non-equidistant (or equidistant) datapoints by linear interpolating and comparing at given interpolation points t_comp. (Zygote-friendly: Zygote can differentiate through via AD.)

Computes all batch element losses. Picks the batch element with the greatest accumulated loss as next training element. If picked, accumulated loss is resetted.
(Prevents starvation of batch elements with little loss)

Writes/Copies flatted (Flux.destructure) training parameters p_net to non-flat model net with data offset c.

Computes all batch element losses. Picks the batch element with the greatest loss as next training element.

ToDo.

Forces a simultaniuos zero crossing together with a given value by function.

ToDo.

ToDo.

Computes all batch element losses. Picks the batch element with the greatest grow in loss (derivative) as next training element.

Sequentially runs over all elements.

A neutral layer that calls a function fct with current FMU time as input.

ToDo.

Picks a random batch element as next training element.