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Found 9 papers, 7 papers with code
A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks
While many physics-based closure model forms have been posited for the sub-filter scale (SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical simulation (DNS) create opportunities to leverage data-driven modeling techniques.
PINN surrogate of Li-ion battery models for parameter inference. Part II: Regularization and application of the pseudo-2D model
To reduce the computational cost of Bayesian calibration, numerical solvers for physics-based models can be replaced with faster surrogates.
PINN surrogate of Li-ion battery models for parameter inference. Part I: Implementation and multi-fidelity hierarchies for the single-particle model
The techniques used to develop a PINN surrogate of the SPM are extended in Part II for the PINN surrogate for the P2D battery model, and explore the Bayesian calibration capabilities of both surrogates.
Ensemble flow reconstruction in the atmospheric boundary layer from spatially limited measurements through latent diffusion models
We find that diffusion models show promise and potential for other applications for other turbulent flow reconstruction problems.
Bi-fidelity Modeling of Uncertain and Partially Unknown Systems using DeepONets
Recent advances in modeling large-scale complex physical systems have shifted research focuses towards data-driven techniques.
Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows
In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods.
GANISP: a GAN-assisted Importance SPlitting Probability Estimator
Genealogical importance splitting reduces the variance of rare event probability estimators by iteratively selecting and replicating realizations that are headed towards a rare event.
Adversarial sampling of unknown and high-dimensional conditional distributions
In that case, it is necessary to sample elements from unknown high-dimensional spaces with possibly millions of degrees of freedom.
Using machine learning to construct velocity fields from OH-PLIF images
Ultimately, this work shows the powerful ability of the CNN to decode the three-dimensional PIV fields from input OH-PLIF images, providing a potential groundwork for a very useful tool for experimental configurations in which accessibility of forms of simultaneous measurements are limited.
