Poster "physics-informed neural networks" Papers
15 papers found
Conference
Consistency of Physics-Informed Neural Networks for Second-Order Elliptic Equations
Yuqian Cheng, Zhuo Chen, Qian Lin
NEURIPS 2025
FreeGave: 3D Physics Learning from Dynamic Videos by Gaussian Velocity
Jinxi Li, Ziyang Song, Siyuan Zhou et al.
CVPR 2025arXiv:2506.07865
5
citations
Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective
Sifan Wang, Ananyae bhartari, Bowen Li et al.
NEURIPS 2025arXiv:2502.00604
38
citations
Hybrid Boundary Physics-Informed Neural Networks for Solving Navier-Stokes Equations with Complex Boundary
ChuYu Zhou, Tianyu Li, Chenxi Lan et al.
NEURIPS 2025arXiv:2507.17535
Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization
Andrés Guzmán-Cordero, Felix Dangel, Gil Goldshlager et al.
NEURIPS 2025arXiv:2505.12149
7
citations
Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems
Sung Woong Cho, Hwijae Son
ICLR 2025arXiv:2412.03161
7
citations
PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations
Namgyu Kang, Jaemin Oh, Youngjoon Hong et al.
ICLR 2025arXiv:2412.05994
8
citations
PINN Balls: Scaling Second-Order Methods for PINNs with Domain Decomposition and Adaptive Sampling
Andrea Bonfanti, Ismael Medina, Roman List et al.
NEURIPS 2025arXiv:2510.21262
PINNs with Learnable Quadrature
Sourav Pal, Kamyar Azizzadenesheli, Vikas Singh
NEURIPS 2025
Robust Weight Initialization for Tanh Neural Networks with Fixed Point Analysis
Hyunwoo Lee, Hayoung Choi, Hyunju Kim
ICLR 2025arXiv:2410.02242
6
citations
Solving Differential Equations with Constrained Learning
Viggo Moro, Luiz Chamon
ICLR 2025arXiv:2410.22796
2
citations
Challenges in Training PINNs: A Loss Landscape Perspective
Pratik Rathore, Weimu Lei, Zachary Frangella et al.
ICML 2024arXiv:2402.01868
116
citations
Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss
Yahong Yang, Juncai He
ICML 2024arXiv:2402.00152
13
citations
Parameterized Physics-informed Neural Networks for Parameterized PDEs
Woojin Cho, Minju Jo, Haksoo Lim et al.
ICML 2024arXiv:2408.09446
44
citations
Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification
Yiming Meng, Ruikun Zhou, Amartya Mukherjee et al.
ICML 2024arXiv:2402.10119
17
citations