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Control Of Physics Informed Gaussian Processes.
A curated list of solvers/software/frameworksrelevant for dynamic optimiation
Solver for Algebraic Riccati equation with two ways: iteration based method & eigenvalue decomposition method
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
Soure code for Deep Koopman with Control
Expectation Maximization (EM) algorithm to approximate Koopman generators for control-affine systems
Learn sparse representations of the Koopman operator in POD basis in kernel feature space
Multiple-model nonlinear regression system identification using the EM algorithm and Bayesian framework for model assignment
Use autoencoder with linear recurrence of encoded state to learn a Koopman invariant subspace of a dynamical system.
Autonomously learning to race autonomously
A general-purpose Python package for Koopman theory using deep learning.
Implementation of Koopman operator theory based pruning in the ShrinkBench framework
MurpheyLab / Kalman
Forked from balzer82/KalmanSome Python Implementations of the Kalman Filter
Some Python Implementations of the Kalman Filter
Codebase associated with paper "Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control"
Fast symbolic computation, code generation, and nonlinear optimization for robotics
Methods for numerical differentiation of noisy data in python
giorgosmamakoukas / Bilinear_system_feedback_control
Forked from jackybowen/Bilinear_system_feedback_controlA package for the sparse identification of nonlinear dynamical systems from data
A package for computing data-driven approximations to the Koopman operator.
Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data
ACADO Toolkit is a software environment and algorithm collection for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct o…
The Python Control Systems Library is a Python module that implements basic operations for analysis and design of feedback control systems.