Search Results for "estimate"

The GNNPCSAFT Web App is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. More info on github repository.

The GNNPCSAFT Chat is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, you can chat with LLM models (Gemini or Ollama) with GNNPCSAFT tools, allowing you to ask questions about the PC-SAFT parameters of various compounds, predict thermodynamic properties, and get insights into the GNNPCSAFT's performance.

ALAMODE is designed for analyzing lattice anharmonicity and lattice thermal conductivity of solids. By using an external DFT package such as VASP and Quantum ESPRESSO, you can extract harmonic and anharmonic force constants straightforwardly with ALAMODE. Using the anharmonic force constants, you can also calculate lattice thermal conductivity from first principles. For more information about ALAMODE, please visit the following webpages: Documentation :...

...The simulation currently takes population extinction as a given because it aims to give us better estimates of necessary quarantine lengths in areas where Medfly is not currently established but does repeatedly invade. Thus the objective of this software is to estimate the time to local extirpation based on known ranges of developmental, survivorship and reproductive parameters, as well as assumed ranges of an eradication pressure parameter. For a complete analysis and estimate of time to extirpation one would execute a large number of individual simulations evenly distributed over parameter space to estimate the time at which an arbitrary percentage of the simulations show extirpation.

A networks-on-chip (NoC) simulator allows designers to early estimate performance (latency and throughput), energy efficiency (average/peak power, average energy per packet) and area of several networks on-chip configurations at different CMOS nodes. This tool is a cycle-accurate simulator and is open-source using SystemC, a C++ plugin, which is used to quickly model complex systems at a higher level but less details than RTL.

SPSens is a complete software package written in C that estimates parameter sensitivities for stochastic models of chemical and biochemical reaction networks using Monte Carlo (MC) stochastic simulations. It is possible to estimate sensitivities with respect to system parameters using the following algorithms: finite difference methods (crude monte carlo, common reaction path, coupled finite differences); likelihood ratio methods; and regularized pathwise derivatives. Additionally the package includes basic stochastic simulation algorithms. The package includes several example networks which can be easily modified for other networks. ...

Power/Energy simulation in SystemC. Powersim is a SystemC class library aimed to the calculation of power and energy consumption of hardware described at system level. To this end C++ operators are monitored and different energy models can be used for each data type. Powersim does not require any change in the application source code. Current version is 0.3.0.

A MATLAB package to simulate sample paths of the solution of a Itô or Stratonovich stochastic differential equation (SDE), compute statistics and estimate the parameters from data. A note of caution: SDE Toolbox is no more developed but it's still downloadable. Its inferential capabilities can be considered surpassed (at best). Actually the parameter estimation methods were already far from the state-of-art when the project began in 2007 (!). The considered implemented parametric and non-parametric Monte Carlo likelihood methods were chosen for their ability to treat both one-dimensional and multivariate SDE systems, although the quality of the inferential results can't match those obtained using more advanced techniques. ...

Algorithms to estimate the number of k-colorings of a low-degree graph using samplings from a markov chain are provided.