Python Dynamic Mode Decomposition
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Updated
Jul 21, 2024 - Python
Python Dynamic Mode Decomposition
pyMOR - Model Order Reduction with Python
ICA-AROMA Software Package: a data-driven method to identify and remove head motion-related artefacts from functional MRI data.
A Python toolbox for quantitative, reproducible flow cytometry analysis
FlowNet - Data-Driven Reservoir Predictions
Framework for Data-Driven Design & Analysis of Structures & Materials (F3DASM)
A unified framework for machine learning collective variables for enhanced sampling simulations
Tensor Train Toolbox
SIEGMA - Transform Sigma rules into SIEM consumables
Python library that implements DeePC: Data-Enabled Predictive Control
A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
A python package for dynamic modeling and simulation of buildings. The framework builds upon the SAREF ontology and data-driven component models.
Python tools for non-intrusive reduced order modeling
MPG is originated from the paper "Mixed policy gradient", which also contains a cluster of high-quality implementations of deep reinforcement learning algorithms.
Curvature Corrected Moving Average: An accurate and model-free path smoothing algorithm.
moai is a PyTorch-based AI Model Development Kit (MDK) created to improve data-driven model workflows, design and reproducibility.
⚡ Instantly turn your python function into web app.
ModelFLOWs application
Package for the data-driven representation of non-linear dynamics over manifolds based on a statistical distribution of local phase portrait features. Includes specific example on dynamical systems, synthetic- and real neural datasets. https://agosztolai.github.io/MARBLE/
This repo contains the code for the paper "Data-driven discovery of multiscale chemical reactions governed by the law of mass action"
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