Papers
1–3 of 3Research Paper·Mar 13, 2026
Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs
Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harder and optimization less stable. The problem becomes ...
7.0 viability
Research Paper·Mar 18, 2026
Symmetry-Reduced Physics-Informed Learning of Tensegrity Dynamics
Tensegrity structures possess intrinsic geometric symmetries that govern their dynamic behavior. However, most existing physics-informed neural network (PINN) approaches for tensegrity dynamics do not...
4.0 viability
Research Paper·Mar 18, 2026
Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer
Accurate prediction of permeability tensors from pore-scale microstructure images is essential for subsurface flow modeling, yet direct numerical simulation requires hours per sample, fundamentally li...
3.0 viability