Physics-Informed Learning Comparison Hub
3 papers - avg viability 4.7
Top Papers
- Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs(7.0)
DLDMF is a physics-informed framework that enhances neural surrogate models for parameterized PDEs by separating space, time, and parameters for improved generalization and extrapolation.
- Symmetry-Reduced Physics-Informed Learning of Tensegrity Dynamics(4.0)
SymPINN leverages symmetry in tensegrity structures to enhance the efficiency and accuracy of physics-informed neural networks for dynamic predictions.
- Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer(3.0)
A physics-informed deep learning framework for predicting permeability tensors from porous media microstructure images.