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Found 8 papers, 2 papers with code
Turbulent scalar flux in inclined jets in crossflow: counter gradient transport and deep learning modelling
This approach uses a deep neural network with embedded coordinate frame invariance to predict a tensorial turbulent diffusivity that is not explicitly available in the high fidelity data used for training.
Machine-learned metrics for predicting the likelihood of success in materials discovery
Materials discovery is often compared to the challenge of finding a needle in a haystack.
Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and Optimization
Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning.
Generalization of machine-learned turbulent heat flux models applied to film cooling flows
The design of film cooling systems relies heavily on Reynolds-Averaged Navier-Stokes (RANS) simulations, which solve for mean quantities and model all turbulent scales.
Overcoming data scarcity with transfer learning
Here, we describe and compare three techniques for transfer learning: multi-task, difference, and explicit latent variable architectures.
Building Data-driven Models with Microstructural Images: Generalization and Interpretability
As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure.
High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates
The optimization of composition and processing to obtain materials that exhibit desirable characteristics has historically relied on a combination of scientist intuition, trial and error, and luck.
A Comprehensive Physics-Informed Machine Learning Framework for Predictive Turbulence Modeling
In this work, we introduce the procedures toward a complete PIML framework for predictive turbulence modeling, including learning Reynolds stress discrepancy function, predicting Reynolds stresses in different flows, and propagating to mean flow fields.
Fluid Dynamics
