2025 Poster "partial differential equations" Papers
18 papers found
ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning
Nilo Schwencke, Cyril Furtlehner
Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations
David Dalton, Alan Lazarus, Hao Gao et al.
Collapsing Taylor Mode Automatic Differentiation
Felix Dangel, Tim Siebert, Marius Zeinhofer et al.
Continuous Simplicial Neural Networks
Aref Einizade, Dorina Thanou, Fragkiskos Malliaros et al.
Gradient-Free Generation for Hard-Constrained Systems
Chaoran Cheng, Boran Han, Danielle Maddix et al.
Hybrid Boundary Physics-Informed Neural Networks for Solving Navier-Stokes Equations with Complex Boundary
ChuYu Zhou, Tianyu Li, Chenxi Lan et al.
Metamizer: A Versatile Neural Optimizer for Fast and Accurate Physics Simulations
Nils Wandel, Stefan Schulz, Reinhard Klein
Minimal Variance Model Aggregation: A principled, non-intrusive, and versatile integration of black box models
Theo Bourdais, Houman Owhadi
Model-Agnostic Knowledge Guided Correction for Improved Neural Surrogate Rollout
Bharat Srikishan, Daniel O'Malley, Mohamed Mehana et al.
Physics-Constrained Flow Matching: Sampling Generative Models with Hard Constraints
Utkarsh Utkarsh, Pengfei Cai, Alan Edelman et al.
Physics-Informed Diffusion Models
Jan-Hendrik Bastek, WaiChing Sun, Dennis Kochmann
PIED: Physics-Informed Experimental Design for Inverse Problems
Apivich Hemachandra, Gregory Kang Ruey Lau, See-Kiong Ng et al.
PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations
Namgyu Kang, Jaemin Oh, Youngjoon Hong et al.
PINNs with Learnable Quadrature
Sourav Pal, Kamyar Azizzadenesheli, Vikas Singh
Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations
Takashi Furuya, Koichi Taniguchi, Satoshi Okuda
Solving Differential Equations with Constrained Learning
Viggo Moro, Luiz Chamon
Solving Partial Differential Equations via Radon Neural Operator
Wenbin Lu, Yihan Chen, Junnan Xu et al.
UGM2N: An Unsupervised and Generalizable Mesh Movement Network via M-Uniform Loss
Zhichao Wang, Xinhai Chen, Qinglin Wang et al.