Poster "partial differential equations" Papers
36 papers found
ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning
Nilo Schwencke, Cyril Furtlehner
Collapsing Taylor Mode Automatic Differentiation
Felix Dangel, Tim Siebert, Marius Zeinhofer 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.
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 Partial Differential Equations via Radon Neural Operator
Wenbin Lu, Yihan Chen, Junnan Xu et al.
$\bf{\Phi}_\textrm{Flow}$: Differentiable Simulations for PyTorch, TensorFlow and Jax
Philipp Holl, Nils Thuerey
Accelerating PDE Data Generation via Differential Operator Action in Solution Space
huanshuo dong, Hong Wang, Haoyang Liu et al.
A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts
Huy Nguyen, Pedram Akbarian, TrungTin Nguyen et al.
Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains
Levi Lingsch, Mike Yan Michelis, Emmanuel de Bézenac et al.
Challenges in Training PINNs: A Loss Landscape Perspective
Pratik Rathore, Weimu Lei, Zachary Frangella et al.
Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss
Yahong Yang, Juncai He
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
Zhongkai Hao, Chang Su, LIU SONGMING et al.
Graph Neural PDE Solvers with Conservation and Similarity-Equivariance
Masanobu Horie, NAOTO MITSUME
HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
Andrey Bryutkin, Jiahao Huang, Zhongying Deng et al.
Liouville Flow Importance Sampler
Yifeng Tian, Nishant Panda, Yen Ting Lin
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
Brooks(Ruijia) Niu, Dongxia Wu, Kai Kim et al.
Neural operators meet conjugate gradients: The FCG-NO method for efficient PDE solving
Alexander Rudikov, Fanaskov Vladimir, Ekaterina Muravleva et al.
Neural Operators with Localized Integral and Differential Kernels
Miguel Liu-Schiaffini, Julius Berner, Boris Bonev et al.
Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields
Tom Fischer, Pascal Peter, Joachim Weickert et al.
Physics and Lie symmetry informed Gaussian processes
David Dalton, Dirk Husmeier, Hao Gao
Physics-Informed Neural Network Policy Iteration: Algorithms, Convergence, and Verification
Yiming Meng, Ruikun Zhou, Amartya Mukherjee et al.
Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning
Junfeng CHEN, Kailiang Wu
Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations
Ze Cheng, Zhongkai Hao, Wang Xiaoqiang et al.
Self-Supervised Coarsening of Unstructured Grid with Automatic Differentiation
Sergei Shumilin, Alexander Ryabov, Nikolay Yavich et al.
TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
Zhuo Chen, Jacob McCarran, Esteban Vizcaino et al.
Towards General Neural Surrogate Solvers with Specialized Neural Accelerators
Chenkai Mao, Robert Lupoiu, Tianxiang Dai et al.
Towards Robust Full Low-bit Quantization of Super Resolution Networks
Denis Makhov, Irina Zhelavskaya, Ruslan Ostapets et al.
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
Chandra Mouli Sekar, Danielle Robinson, Shima Alizadeh et al.