QibangLiu / GINOTView external linksLinks
GINOT is a deep learning model that combines transformers with neural operators for accurate forward predictions on arbitrary 2D and 3D geometries. It processes surface point clouds using attention-based encoding with sampling and grouping, ensuring robustness to point density, order invariance, and padding resilience.
☆24Jan 20, 2026Updated 3 weeks ago
Alternatives and similar repositories for GINOT
Users that are interested in GINOT are comparing it to the libraries listed below
Sorting:
- implementation of physics-informed variational auto-encoder☆20Oct 26, 2023Updated 2 years ago
- Here you find the Spectral Operator Learning Under construcTION☆20May 8, 2024Updated last year
- Graph-based operator learning in arbitrary geometries☆34Dec 19, 2025Updated last month
- Papers and codes of Physics-informed Deep Compositional Operator Network☆13Oct 31, 2025Updated 3 months ago
- Code of ICML paper arxiv.org/abs/2302.08105☆14May 4, 2023Updated 2 years ago
- Code for Point-Calibrated Spectral Neural Operators☆20Oct 15, 2024Updated last year
- [NeurIPS 2024 Spotlight] Towards Universal Mesh Movement Networks☆19Jul 16, 2025Updated 6 months ago
- Domain Agnostic Fourier Neural Operators (DAFNO)☆19Sep 3, 2024Updated last year
- ☆24Mar 15, 2024Updated last year
- Differentiable Flexible Mechanical Metamaterials☆31Updated this week
- PyTorch implemention of the Position-induced Transformer for operator learning in partial differential equations☆25Jun 3, 2025Updated 8 months ago
- Code for "Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains"☆24May 13, 2024Updated last year
- ☆90Sep 2, 2024Updated last year
- [ICML 2023] Non-Uniform Neural Operator (NUNO)☆26Jul 3, 2023Updated 2 years ago
- A Library for Advanced Neural PDE Solvers.☆264Nov 17, 2025Updated 2 months ago
- This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs☆207Nov 24, 2025Updated 2 months ago
- Time- and space-continuous neural PDE forecaster based on INRs and ODEs - ICLR 2023☆73Jan 13, 2024Updated 2 years ago
- Addressing the problem of predicting crime occurrence based on historic records☆11Nov 27, 2019Updated 6 years ago
- ☆29Jul 8, 2024Updated last year
- The official implementation of ``CKGConv: General Graph Convolution with Continuous Kernels'' (ICML 2024)☆32Aug 5, 2024Updated last year
- This repository contains code for the paper "MAgNet: Mesh-Agnostic Neural PDE Solver" https://arxiv.org/abs/2210.05495☆38Jun 21, 2023Updated 2 years ago
- About code release of "Transolver: A Fast Transformer Solver for PDEs on General Geometries", ICML 2024 Spotlight. https://arxiv.org/abs/…☆263Feb 6, 2026Updated last week
- About Code Release for "Solving High-Dimensional PDEs with Latent Spectral Models" (ICML 2023), https://arxiv.org/abs/2301.12664☆79Apr 10, 2025Updated 10 months ago
- ☆13Jun 18, 2025Updated 7 months ago
- Differentiable neural-network solver for data assimilation of ice shelves written in JAX☆39May 12, 2025Updated 9 months ago
- A python code for 3d topology optimization using MMA optimizers in NLOPT☆11Feb 9, 2017Updated 9 years ago
- Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems [ICML'25]☆111Oct 11, 2025Updated 4 months ago
- Solve sparse linear systems in JAX using the KLU algorithm☆38Updated this week
- The code is for two phase demonstration as example 1 shown in the paper - Gao, Yi, and Yongming Liu. "Reliability-based topology optimiza…☆11Dec 27, 2021Updated 4 years ago
- A python code for 2d topology optimization using MMA optimizers in NLOPT☆12Feb 9, 2017Updated 9 years ago
- ☆13Aug 28, 2024Updated last year
- Code for Mesh Transformer describes in the EAGLE dataset☆42Feb 21, 2025Updated 11 months ago
- Code for AAAI21 paper "Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning"☆11Feb 15, 2022Updated 4 years ago
- AeroTop: an efficient aerodynamic topology optimization framework☆12Apr 1, 2022Updated 3 years ago
- This is the code of paper: Robust Mid-Pass Filtering Graph Convolutional Networks.(paper accepted by WWW2023)☆13Feb 17, 2023Updated 2 years ago
- The official PyTorch implementation of "An Attentional Multi-scale Co-evolving Model for Dynamic Link Prediction" (TheWebConf'23)☆11May 4, 2023Updated 2 years ago
- Code for ICML21 paper "Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation"☆12Feb 8, 2023Updated 3 years ago
- Mitigating the Filter Bubble while Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems☆10Mar 15, 2023Updated 2 years ago
- ☆13Nov 29, 2021Updated 4 years ago