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files implementation » time implementation (Expand Search), pilot implementation (Expand Search), assess implementation (Expand Search)
python model » python tool (Expand Search), action model (Expand Search), motion model (Expand Search)
files implementation » time implementation (Expand Search), pilot implementation (Expand Search), assess implementation (Expand Search)
python model » python tool (Expand Search), action model (Expand Search), motion model (Expand Search)
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Jaxkineticmodel allows for hybridizing kinetic models with neural networks.
Published 2025Subjects: -
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BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories
Published 2025“…We believe that BaNDyT is the first software package to include specialized and advanced features for analyzing MD simulation trajectories using a probabilistic graphical network model. We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”
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BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories
Published 2025“…We believe that BaNDyT is the first software package to include specialized and advanced features for analyzing MD simulation trajectories using a probabilistic graphical network model. We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”
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BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories
Published 2025“…We believe that BaNDyT is the first software package to include specialized and advanced features for analyzing MD simulation trajectories using a probabilistic graphical network model. We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”
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code implementing the finite element method and finite difference method from Hybrid PDE-ODE Models for Efficient Simulation of Infection Spread in Epidemiology
Published 2025“…This dataset contains code implementing the finite element method based on Kaskade 7 (C++) and code implementing the finite difference method (Python) for the development of hybrid PDE-ODE models aimed at efficiently simulating infection spread in epidemiology. …”
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ALAAM models for the Deezer networks, with liking “alternative” music as the outcome variable.
Published 2024Subjects: -
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Models estimated using ALAAMEE for the Deezer networks, with liking jazz as the outcome variable.
Published 2024Subjects: -
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Exploring the integration of metaverse technologies in engineering education through the SAMR model
Published 2025“…The final deliverable is a plan for phased integration of metaverse learning into a Python programming course following this model, building on existing best practices.…”
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EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit
Published 2025“…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”
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The format of the simulation reports
Published 2025“…To enable efficient calculation of extracellular signals from large neural network simulations, we have developed <i>BlueRecording</i>, a pipeline consisting of standalone Python code, along with extensions to the Neurodamus simulation control application, the CoreNEURON computation engine, and the SONATA data format, to permit online calculation of such signals. …”
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Comparison of BlueRecording with existing tools
Published 2025“…To enable efficient calculation of extracellular signals from large neural network simulations, we have developed <i>BlueRecording</i>, a pipeline consisting of standalone Python code, along with extensions to the Neurodamus simulation control application, the CoreNEURON computation engine, and the SONATA data format, to permit online calculation of such signals. …”
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ZILLNB_Model
Published 2025“…<p dir="ltr">Acquire latent variables using deep-learning based model implemented in python</p>…”
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Cost functions implemented in Neuroptimus.
Published 2024“…To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. …”
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