Future-Prediction-in-Dynamic-Simulations-via-DL | Computational cost
kandi X-RAY | Future-Prediction-in-Dynamic-Simulations-via-DL Summary
kandi X-RAY | Future-Prediction-in-Dynamic-Simulations-via-DL Summary
Future-Prediction-in-Dynamic-Simulations-via-DL is a Python library. Future-Prediction-in-Dynamic-Simulations-via-DL has no bugs, it has no vulnerabilities and it has low support. However Future-Prediction-in-Dynamic-Simulations-via-DL build file is not available. You can download it from GitHub.
Computational cost of large and lengthy Brownian Dynamics simulations is a principle challenged faced by scientific researchers. In this work, I utilize deep residual networks to alleviate the computational cost of dynamic simulations by circumventing the computation of every time step and predicting future states of a simulation system. I present qualitative and quantitative evaluations of predictions, which are able to replicate the accuracy of simulations with a system size of 8,788 particles. This novel method for reducing the cost of simulations sets a foundation for a new avenue of deep learning application in the research of physical sciences.
Computational cost of large and lengthy Brownian Dynamics simulations is a principle challenged faced by scientific researchers. In this work, I utilize deep residual networks to alleviate the computational cost of dynamic simulations by circumventing the computation of every time step and predicting future states of a simulation system. I present qualitative and quantitative evaluations of predictions, which are able to replicate the accuracy of simulations with a system size of 8,788 particles. This novel method for reducing the cost of simulations sets a foundation for a new avenue of deep learning application in the research of physical sciences.
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Future-Prediction-in-Dynamic-Simulations-via-DL has a low active ecosystem.
It has 0 star(s) with 0 fork(s). There are no watchers for this library.
It had no major release in the last 6 months.
Future-Prediction-in-Dynamic-Simulations-via-DL has no issues reported. There are no pull requests.
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The latest version of Future-Prediction-in-Dynamic-Simulations-via-DL is current.
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Future-Prediction-in-Dynamic-Simulations-via-DL has no bugs reported.
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Future-Prediction-in-Dynamic-Simulations-via-DL has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
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Future-Prediction-in-Dynamic-Simulations-via-DL does not have a standard license declared.
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Future-Prediction-in-Dynamic-Simulations-via-DL has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions are not available. Examples and code snippets are available.
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Install Future-Prediction-in-Dynamic-Simulations-via-DL
You can download it from GitHub.
You can use Future-Prediction-in-Dynamic-Simulations-via-DL like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use Future-Prediction-in-Dynamic-Simulations-via-DL like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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