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Found 7 papers, 1 papers with code
Locally Adaptive and Differentiable Regression
Over-parameterized models like deep nets and random forests have become very popular in machine learning.
Differentiable Turbulence II
Differentiable fluid simulators are increasingly demonstrating value as useful tools for developing data-driven models in computational fluid dynamics (CFD).
Differentiable Turbulence: Closure as a partial differential equation constrained optimization
Deep learning is increasingly becoming a promising pathway to improving the accuracy of sub-grid scale (SGS) turbulence closure models for large eddy simulations (LES).
Importance of equivariant and invariant symmetries for fluid flow modeling
Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based simulations of physical systems, including fluid dynamics.
Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine Learning
The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes.
Differentiable physics-enabled closure modeling for Burgers' turbulence
Data-driven turbulence modeling is experiencing a surge in interest following algorithmic and hardware developments in the data sciences.
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations
Finally, we also demonstrate that similar results can be obtained for the PINN solution to the heat equation (a space-time problem) by discretizing the spatial derivatives using RBF-FD and using automatic differentiation for the temporal derivative.
