Accept environment path for info.
Drop PDESurrogates from JuliaSim.
Upgrade to Julia v1.9.3.
When creating new Pluto notebooks on JuliaHub with JuliaSim, the cells necessary to make a notebook work with JuliaSim are automatically added to the notebook.

JuliaSimBatteries

Add support for time- and state-varying experimental control inputs.
Reduce the package compilation times.
Improve documentation landing page and model comparisons.

JuliaSimControl

Improve fixed step integrator providing additional features.
Improve documentation structure, including video tutorials.
Reduce complexity of various APIs.

JuliaSimModelOptimizer

Add support for multiple models in the same InverseProblem.
Add support for Prediction Error Method which is very useful for unstable systems.
Improvements in performance, stability and correctness of Collocation Methods.
Overall stability in the API for generic use cases.

v0.29.0

JuliaSimControl

Features

Add Polynomial-Quadratic Regulator control design (PQR)
Add function common_lyap that computes quadratic Lyapunov functions for uncertain systems
Add polynomial trajectory synthesizer
Add function poly_approx for least-squares polynomial approximation of nonlinear dynamics

Tutorials

JuliaSimModelOptimizer

Features

Add support for DataSets as input data
Add symbolic regression interface for automated model discovery
Add support for log transformed search space
Add parallelization support for multiple shooting

JuliaSimSurrogates

Features

Add support for custom callbacks in training loop
Add support for external simulators like JuliaSimBatteries
Add support for plotting ExperimentData objects directly
Add support to spawn data generation and training jobs on JuliaHub
Added a Datagen app for tuning controllers
DigitalEcho now supports deploying to MTK
Generically handle constant values during normalization for the DigitalEcho
Implemented Controller which has a fixed value at t=0
Improved documentation
Improves the performance of DigitalEcho
Scalable storage format for data generation
Support for training with GPUs

PumasQSP

Features

Add support for DataSets as input data
Add model autocomplete
- You can now add a neural network to your model and train it as a calibration problem
- The symbolic regression interface can be used to interpret the results from the neural network
Add support for log transformed search space
Add parallelization support for multiple shooting
Add support for dosing tables

v0.28.0

JuliaSim

Features

Upgrade to Julia v1.8.5.

JuliaSimControl

Features

Add sliding mode controllers.
Add support for integer and binary variables in Model Predictive Controllers (MPC).
Introduce Linear Quadratic Regulator (LQR) design method for uncertain systems.
Introduce robust Model Predictive Control (MPC) for systems with uncertain parameters.

JuliaSimModelOptimizer

Features

Add model autocomplete
- You can now add a neural network to your model and train it as a calibration problem
- This can be used for automatic model discovery
Experimental Import FMUs into inverse problems
- Having an MTK model as a starting point is no longer required as we can now import both model exchange and co-simulation FMUs.

v0.27.0

JuliaSimModelOptimizer

Breaking changes

Renamed structures to better fit the engineering domain.
- Trial -> `Experiment
- SteadyStateTrial -> SteadyStateExperiment
- ComparisonTrial -> ExperimentComparison
- Renamed trial collections to experiment collections
  - IndependentTrials -> IndependentExperiments
  - SteadyStateTrials -> SteadyStateExperiments
  - TrialChains -> ChainedExperiments

Features

Add support for parameter priors.
Add parametrization support to the initial conditions.
Add ReplicateData. This allows one to use multiple datasets for one experiment.
Add experiment chains. Experiments can now have arbitrarily deep dependencies on any other previously defined experiments.

PumasQSP

Features

Add support for parameter priors.
Add parametrization support to the initial conditions.
Add the possibility to import PEtab files.
Add MAPEL.
Add ReplicateData. This allows one to use multiple datasets for one trial.
Add trial chains. Trials can now have arbitrarily deep dependencies on any other previously defined trials.

v0.26.0

JuliaSim

Breaking Change

Requires at least JuliaHub v6.0.0 as the deployment platform.

v0.25.1

JuliaSimModelOptimizer

Add error handling in calibration.
Decreased overhead for small problems.
Fix a bug in how parameters and initial conditions were computed, when ForwardDiff was used, which could have lead to wrong results.
Fix MultipleShooting bugs that could lead to wrong results.

BuildingModelLibrary

Bumps the CSV version to v0.10 (from earlier v0.9)

ThermalThermofluid

Adds LICENSE
Fixes room iteration order

v0.25.0

JuliaSimModelOptimizer

Features

Hierarchical Bayesian inference is now available as a virtual population generation method.
This method assumes that the parameters of each Trial subject are sampled from the same population distribution. It then calculates the posterior distribution all parameters, subject-specific and population ones, given the available data. Finally, it uses the posterior distribution of population-level parameters to generate a virtual population.
When using a Bayesian method to generate a virtual population with MCMCOpt, if the keyword argument hierarchical = true, then this hierarchical method will be used.
Pathfinder MCMC initialization is now available. This is an alternative to the standard warmup phase that sampling methods such as No-U-Turn-Sampler use to find regions of high probability mass and tune some internal parameters before sampling. In many cases Pathfinder is able to find regions of more probable parameter values more efficiently.
Users can choose this method by setting the keyword argument warmup_method = PathfinderWarmUp() in MCMCOpt, before passing it to the vpop function, otherwise the default warmup_method = StandardWarmUp() will be used.

v0.24.1

JuliaSimSurrogates

Fix broken surrogates dashboard styling.

v0.24.0

JuliaSimControl

The JuliaSimControls package has been renamed to JuliaSimControl. All references to the package should be updated accordingly.
Trimming of ModelingToolkit (MTK) models.
More general Model-Predictive Control (MPC) problem formulation.

JuliaSimModelOptimizer

New calibration feature for robustly fitting data using multiple shooting and collocation methods.

PumasQSP

Add support for dosing. The following types of doses can be used:
- Bolus, increment a state by some amount.
- PeriodicBolus, same as Bolus periodically.
- Infusion, increments the rate of change of a state by some amount for a time window of given duration.
- PeriodicInfusion, same as Infusion periodically, where the period counts from the onset time of the Infusion time window.

v0.23.0

PumasQSP

New population subsampling method
- Users can now set target distributions for any number of model states and our new custom-made method will subsample a virtual population, so that the same model states of virtual patients match the target distributions, as measured by discretized histograms.
New plot recipe to plot all trials from a virtual population.
Improvements to plotting functionality.

v0.22.0

PumasQSP

The initial release of PumasQSP contains the following features:

Bayesian inference is now available when generating virtual populations
- Each Trial object now contains a likelihood function and noise_priors distributions, which together describe how data was measured in a trial.
Timespan optimization is now possible
- The timespan of a given trial can be fit to data, similarly to how model parameters and initial conditions are optimized to match the data of the trial.
New subsampling API
- Subsampling a virtual population of patients vp for a given trial can now be performed with a simple call to subsample(alg, vp, trial), where alg is one of several available subsampling methods, like MAPEL or Allen-Reiger-Musante 2016 ARM.
Accessing the sampler of each subsampling method
- Users now have access to a Sampler object, which they can call repeatedly to generate new subsampled virtual populations.
- This way, users can perform subsampling multiple times quickly, without having to wait for the necessary computations of the respective method to finish every time (e.g. solving an optimization problem, like in the case of Allen-Reiger-Musante subsampling).