Release notes

Enhancement: Now supports integration of all Lightning hooks into the Neuromancer Lightning trainer. Please refer to Lightning examples README for more information
Deprecated WandB hyperparameter tuning via LitTrainer for now
New feature: TorchSDE integration with Neuromancer core library, namely torchsde.sdeint(). Motivating example for system ID on stochastic process found in examples/sdes/sde_walkthrough.ipynb
New feature: Stacked physics-informed neural networks
New feature: SINDy -- sparse system identification of nonlinear dynamical systems
New feature: differentiable proximal operators in operator splitting methods for learning to optimize

New Feature: PyTorch Lightning Integration with NeuroMANCER core library. All these features are opt-in.
- Code simplifications: zero boilerplate code, increased modularity
- Added ability for user to define custom training logic
- Easy support for GPU and multi-GPU training
- Easy Weights and Biases (https://wandb.ai/site) hyperparameter tuning and Tensorboard Logging

New feature: Update violation energy for projected gradient #110 (based on idea #86).
Reverted psl.nonautonomous.TwoTank (umin, umax) bounds to (0.5, 0.5) for numerical stability #105
Added new unit tests for problem.py and system.py #107
Automated docs build from master -> gh-pages #107
Fixed positional arg error and added support for Time data in file_emulator.py #119
Fixed a bug in System which caused incorrect visualization of the computational graph
New examples:
Other minor updates to examples

To simplify integration, interpolation of control input is no longer supported in integrators.py
- The interp_u parameter of Integrator and subclasses has been deprecated
Additional inputs (e.g., u, t) can now be passed as *args (instead of as a single tensor input stacked with x) in:
- Integrator and subclasses in integrators.py
- Block - new base class for all other classes in blocks.py
- ODESystem in ode.py
New Physics-Informed Neural Network (PINN) examples for solving PDEs in /examples/PDEs/
New system identification examples for ODEs in /examples/ODEs/
Fixed a bug in the show(...) method of the Problem class
Hotfix: *args for GeneralNetworkedODE

Refactored PSL
- Better PSL unit testing coverage
- Consistent interfaces across system types
- Consistent perturbation signal interface in signals.py
Refactored Control and System ID learning using Node and System class (system.py)
- Classes used for system ID can now be easily interchanged to accommodate downstream control policy learning

Merged Structured Linear Maps and Pyton Systems Library into Neuromancer
- The code in neuromancer was closely tied to psl and slim. A decision was made to integrate the packages as submodules of neuromancer. This also solves the issue of the package names "psl" and "slim" already being taken on PyPI.

Import changes for psl and slim

# before
import psl
import slim

# now
from neuromancer import psl
from neuromancer import slim

New example scripts and notebooks
- Interactive Colab notebooks for testing Neuromancer functionality without setting up an environment
  - See Examples for links to Colab
- RC-Network modeling using Graph Neural Time-steppers example:
  - See neuromancer/examples/graph_timesteppers/
- Baseline NODE dynamics modeling results for all nonautonomous systems in Python Systems Library
  - See neuromancer/examples/benchmarks/node/
- Updated install instructions for Linux, Windows, and MAC operating systems
  - New linux_env.yml, windows_env.yml, osxarm64_env.yml files for installation of dependencies across OS
Corresponding releases of SLiM and PSL packages
- Make sure to update these packages if updating Neuromancer
- Release 1.4 will roll SLiM and PSL into core Neuromancer for ease of installation and development

Tutorial YouTube videos to accompany tutorial scripts in examples folder:
- examples/system_identification/duffing_parameter.py
Closed loop control policy learning examples with Neural Ordinary Differential Equations
- examples/control/
  - vdpo_DPC_cl_fixed_ref.py
  - two_tank_sysID_DPC_cl_var_ref.py
  - two_tank_DPC_cl_var_ref.py
  - two_tank_DPC_cl_fixed_ref.py
Closed loop control policy learning example with Linear State Space Models.
- examples/control/
  - double_integrator_dpc_ol_fixed_ref.py
  - vtol_dpc_ol_fixed_ref.py
New class for Linear State Space Models (LSSM)
- LinearSSM in dynamics.py
Refactored closed-loop control policy simulations
- simulator.py
Interfaces for open and closed loop simulation (evaluation after training) for several classes
- Dynamics
- Estimator
- Policy
- Constraint
- PSL Emulator classes
New class for closed-loop policy learning of non-autonomous ODE systems
- ControlODE class in ode.py
Added support for NODE systems
- Torchdiffeq integration with fast adjoint method for NODE optimization

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