forgi86

dynonet

"dynoNet: A neural network architecture for learning dynamical systems" by Marco Forgione and Dario Piga

efficient-calibration-embedded-MPC

Python code of the paper "Efficient Calibration of Embedded MPC" (2020 IFAC World Congress) by Marco Forgione, Dario Piga, and Alberto Bemporad

lru-reduction

Python code of the paper Model order reduction of deep structured state-space models: A system-theoretic approach

pyMPC

A Model Predictive Control (MPC) Python library based on the OSQP solver.

pytorch-ident

System identification in PyTorch

sysid-neural-continuous

Continuous-time system identification with neural networks

sysid-neural-estimator

Code accompanying the paper "Learning neural state-space models: do we need a state estimator?"

sysid-neural-structures-fitting

Python code of the paper "Model structures and fitting criteria for system identification with neural networks" by Marco Forgione and Dario Piga.

sysid-transfer-functions-pytorch

Codes accompanying the paper "Deep learning with transfer functions: new applications in system identification"

sysid-transformers

Code to reproduce the results of the paper In-context learning for model-free system identification

forgi86/pyMPC

A Model Predictive Control (MPC) Python library based on the OSQP solver.

forgi86/dynonet

"dynoNet: A neural network architecture for learning dynamical systems" by Marco Forgione and Dario Piga

forgi86/sysid-neural-continuous

Continuous-time system identification with neural networks

forgi86/pytorch-ident

System identification in PyTorch

forgi86/sysid-neural-structures-fitting

Python code of the paper "Model structures and fitting criteria for system identification with neural networks" by Marco Forgione and Dario Piga.

forgi86/sysid-transfer-functions-pytorch

Codes accompanying the paper "Deep learning with transfer functions: new applications in system identification"

forgi86/sysid-transformers

Code to reproduce the results of the paper In-context learning for model-free system identification

forgi86/lru-reduction

Python code of the paper Model order reduction of deep structured state-space models: A system-theoretic approach

forgi86/sysid-neural-estimator

Code accompanying the paper "Learning neural state-space models: do we need a state estimator?"

forgi86/RNN-adaptation

Code accompanying the paper "On the adaptation of recurrent neural networks for system identification"

forgi86/sysid-neural-unc

Code of the paper "Neural state-space models: Empirical evaluation of uncertainty quantification"

forgi86/jax-ident

An experimental code base for system identification with Jax.

forgi86/python-control

The Python Control Systems Library is a Python module that implements basic operations for analysis and design of feedback control systems.

forgi86/sysid-transformers-transfer

Code of the paper "On the adaptation of in-context learners for system identification" by Dario Piga, Filippo Pura, and Marco Forgione

forgi86/bayesian-optimization-examples

Bayesian Optimization examples

forgi86/sysid-pytorch-lru

Unofficial PyTorch implementation of DeepMind's LRU with a system identification example.

forgi86/calcolo_mutuo

forgi86/multilevel-excitation

Codes accompanying the paper "Experiment design for parameter estimation in nonlinear systems based on multilevel excitation"

forgi86/BAP

Bayesian Analysis with Python (Second Edition)

forgi86/bayes

Neat Bayesian machine learning examples

forgi86/diffrax

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

forgi86/doepy

Design of Experiment Generator. Read the docs at: https://doepy.readthedocs.io/en/latest/

forgi86/drbayes

Code Repo for "Subspace Inference for Bayesian Deep Learning"

forgi86/extension-cpp

C++ extensions in PyTorch

forgi86/GLIS

GLIS package

forgi86/mixture-density-network

Mixture density network implemented in PyTorch.

forgi86/s4

Structured state space sequence models

forgi86/SpaceProject

Repository for the data challenge project on the space race with Dyuman Bulloni

forgi86/swa_gaussian

Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

forgi86/VariationalRecurrentNeuralNetwork

Pytorch implementation of the Variational Recurrent Neural Network (VRNN).