Skip to content

Latest commit

 

History

History
117 lines (103 loc) · 6.13 KB

RELEASE_NOTES.md

File metadata and controls

117 lines (103 loc) · 6.13 KB
Raw

Release notes

Version 1.5.2 Release Notes

  • New feature: Multi-fidelty Kolgomorov Arnold Networks for SOTA function approximation
  • New feature: Load forecasting on building energy systems tutorials
  • New feature: Transformer block

Version 1.5.1 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

Version 1.5.0 Release Notes

  • 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

Version 1.4.2 Release Notes

Version 1.4.1 Release Notes

  • 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

Version 1.4 Release Notes

  • 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

Version 1.3.2 Release Notes

  • 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

Version 1.3.1 release notes

  • New example scripts and notebooks
    • Interactive Colab notebooks for testing Neuromancer functionality without setting up an environment
    • 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

Version 1.3 release notes

  • Tutorial YouTube videos to accompany tutorial scripts in examples folder:
  • 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