2 citations · 3 across the 24 of their papers we have counts for
Showing 2026 · cs.LGShow all
3 papers · 2 filters
cs.LG2026
Mitigating Gradient Pathology in PINNs through Aligned Constraint
Yichen Luo, Peiyu Zhu, Dongxiao Hu +5
While Physics-Informed Neural Networks (PINNs) are powerful for solving Partial Differential Equations (PDEs), their training is often paralyzed by gradient pathology. The gradient…
cs.LG2026
One step further with Monte-Carlo sampler to guide diffusion better
Minsi Ren, Wenhao Deng, Ruiqi Feng +1
Stochastic differential equation (SDE)-based generative models have achieved substantial progress in conditional generation via training-free differentiable loss-guided approaches.…
cs.LG2026
GenCP: Towards Generative Modeling Paradigm of Coupled Physics
Tianrun Gao, Haoren Zheng, Wenhao Deng +5
Real-world physical systems are inherently complex, often involving the coupling of multiple physics, making their simulation both highly valuable and challenging. Many mainstream…