ICML Poster "partial differential equations" Papers
22 papers found
$\bf{\Phi}_\textrm{Flow}$: 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.
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
Chandra Mouli Sekar, Danielle Robinson, Shima Alizadeh et al.