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nschaetti / EchoTorch

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A Python toolkit for Reservoir Computing and Echo State Network experimentation based on pyTorch. EchoTorch is the only Python module available to easily create Deep Reservoir Computing models.
python machine-learning machine-learning-algorithms torch pytorch recurrent-neural-networks artificial-intelligence recurrent-networks neural-networks python-toolkit artificial-neural-networks machinelearning echo-state-networks reservoir-computing
  • Updated Jul 27, 2021
  • Python
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okozelsk / NET

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Reservoir computing library for .NET. Enables ESN , LSM and hybrid RNNs using analog and spiking neurons working together.
machine-learning deep-learning csharp regression prediction recurrent-neural-networks classification machine-learning-library feedforward-neural-network spiking-neural-networks echo-state-networks dotnet-standard reservoir-computing multiclass-classification time-series-classification liquid-state-machines time-series-forecasting activation-functions
  • Updated Mar 6, 2021
  • C#

Saran-nns / sorn

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PyPi Package of Self-Organizing Recurrent Neural Networks (SORN) and Neuro-robotics using OpenAI Gym
reinforcement-learning artificial-intelligence memory-network complex-networks reservoir-computing neuroinformatics hopfield-network hebbian-learning autonomous-agents cortical-learning cortical-network point-neurons self-organizing-network neuroplasticity bolzmann-machines neuro-computing brain-inspired-computing sorn self-organizing-criticality
  • Updated Jul 31, 2021
  • Python

FoamScience / OpenRSR

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A simple extension to model flow in well pipes

FoamScience
FoamScience commented Aug 7, 2019

This actually was suggested during defending my thesis 😄

Wells can be operated in BHP mode; i.e. One can specify a BHP for a well to respect at all times. But it may seem more natural to specify the Pressure at Well Head instead. The idea is to specify a WHP and necessary Tubing parameters and calculate the corresponding BHP.

  • This will make up a good "OpenFOAM Programming Tutoria
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enhancement good first issue

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