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kerykeion

Data-Driven Astrology 💫 Kerykeion is a Python library for astrology. It generates SVG charts and extracts detailed structured data for birth charts, synastry, transits, composite charts, and more.

pydmd

Python Dynamic Mode Decomposition

pymor

pyMOR - Model Order Reduction with Python

boolean-solver

Data Driven Development

ezyrb

Easy Reduced Basis method

cytoflow

Python tools for quantitative, reproducible flow cytometry analysis

f3dasm

f3dasm - Framework for Data-driven Development and Analysis of Structures and Materials

moai-mdk

moai: Accelerated, Flexible, Modular, Reproducible, Insightful AI

piscola

Supernova light-curve fitting code

galpynostatic

A Python/C++ package with physics-based and data-driven models to predict optimal conditions for fast-charging lithium-ion batteries.

mlcolvar

A unified framework for machine learning collective variables for enhanced sampling simulations

deepctools

A wrapped package for Data-enabled predictive control (DeePC) implementation. Including DeePC and Robust DeePC design with multiple objective functions.

pyloworder

High-performance low-order modeling with Python

direct-data-driven-mpc

Robust and nonlinear Direct Data-Driven MPC controllers for LTI and nonlinear systems in Python

flownet

Simplified training of reservoir simulation models

shatter

Data Driven Development

dado

Data-driven test decorator

phenotrex

Microbial Phenotype Prediction, successor to PICA, implemented with Python 3.7 and scikit-learn

twin4build

Dynamic simulation of buildings with differentiable data-driven models and integration with semantic models.

hydra-moai-searchpath-plugin

moai Hydra SearchPath plugin

macchiato

Data-driven nearest neighbor models for predicting experimental results on silicon lithium-ion battery anodes.

robist

Simple, yet effective, data-driven algorithm for optimization under parametric uncertainty

pythonvrft

Virtual Reference Feedback Tuning (VRFT) Python Library - Alessio Russo (alessior@kth.se)

weidmd

Predicting rate kernels via dynamic mode decomposition. Code for article [https://doi.org/10.1063/5.0170512].