A library for scientific machine learning and physics-informed learning

Dynamical movement primitives (DMPs), probabilistic movement primitives (ProMPs), and spatially coupled bimanual DMPs for imitation learning.

AI4DI - Artificial Intelligence of Digitising Industry: This project builds a docker environment with a running server to trigger a simulation, based on an FMU model. The FMU model can be controlle…

Physics-Guided Machine Learning for Modelling of Building Energy System

Modelica example model for hardware-in-the-loop using dSpace

Physics-Informed Neural networks for Advanced modeling

NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO

Decomposing and Editing Predictions by Modeling Model Computation

A curated list of resources for using LLMs to develop more competitive grant applications.

Wield this tool to be King Arthur of your models.

Genetic Programming in Python, with a scikit-learn inspired API

Safe Bayesian Optimization

Reinforcement Learning with Model Predictive Control

Safe MPC using Learned Viability

Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.

Surrogate modeling and optimization for scientific machine learning (SciML)

pycombina - Solving binary approximation problems in Python

Multiobjective black-box optimization using gradient-boosted trees

a novel framework based on a physics-informed neural network dubbed as PhysCon that combines the interpretable ability of physical laws and the expressive power of neural networks for control-orien…

RaPId (a recursive acronym for "Rapid Parameter Identification") utilizes different optimization and simulation technologies to provide a framework for model validation and calibration of any kind …

A package for computing data-driven approximations to the Koopman operator.

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

Optimal Sparse Decision Trees

ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based m…

Generalized Optimal Sparse Decision Trees

A python library for decision tree visualization and model interpretation.

Python implementation of the Learning Time-Series Shapelets method, that learns a shapelet-based time-series classifier with gradient descent.

Fast SHAP value computation for interpreting tree-based models