Papers
1–2 of 2Research Paper·Jan 15, 2026
SPIKE: Sparse Koopman Regularization for Physics-Informed Neural Networks
Physics-Informed Neural Networks (PINNs) provide a mesh-free approach for solving differential equations by embedding physical constraints into neural network training. However, PINNs tend to overfit ...
5.0 viability
Research Paper·Jan 29, 2026
PILD: Physics-Informed Learning via Diffusion
Diffusion models have emerged as powerful generative tools for modeling complex data distributions, yet their purely data-driven nature limits applicability in practical engineering and scientific pro...
5.0 viability