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Found 4 papers, 0 papers with code
Learning thermodynamically constrained equations of state with uncertainty
Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty quantification (UQ) to improve confidence in the EOS predictions.
Suppressing simulation bias using multi-modal data
The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.
Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations
Large-scale numerical simulations are used across many scientific disciplines to facilitate experimental development and provide insights into underlying physical processes, but they come with a significant computational cost.
Enabling Machine Learning-Ready HPC Ensembles with Merlin
With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data.
