☆27Jul 7, 2022Updated 4 years ago
Alternatives and similar repositories for DeepONet_pytorch
Users that are interested in DeepONet_pytorch are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- This repository contains the code for the paper: Deciphering and integrating invariants for neural operator learning with various physica…☆13Mar 18, 2024Updated 2 years ago
- Simple demo on implementing data driven and physics informed Deep O Nets in pytorch☆22Jun 20, 2024Updated 2 years ago
- A hybrid Decoder-DeepONet operator regression framework for unaligned observation data☆10Jul 16, 2023Updated 3 years ago
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆43Jul 12, 2023Updated 3 years ago
- Contains implementation of PINN using Tensorflow 2.4.0☆14Apr 28, 2023Updated 3 years ago
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- ☆17Jan 18, 2024Updated 2 years ago
- A Deep Learning Approach to Solving PDEs: Implementing Neural Networks with Pytorch and Jax☆10Apr 27, 2023Updated 3 years ago
- 本项目利用pytorch实现了一个简易的模块化DeepONet，凭此可以快速的实现一些简易的DeepONet。☆18Jul 2, 2025Updated last year
- DON-LSTM: Multi-Resolution Learning with DeepONets and Long-Short Term Memory Neural Networks☆10Sep 4, 2025Updated 11 months ago
- Implementation of the deep operator network in pytorch, with examples of solving Differential Equations☆17Mar 30, 2024Updated 2 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆84Aug 4, 2023Updated 3 years ago
- Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robus…☆39Dec 29, 2023Updated 2 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆30Jun 4, 2023Updated 3 years ago
- PINN Implementation for IJCAI paper, "Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activat…☆22Jun 19, 2024Updated 2 years ago
- GPUs on demand by Runpod - Special Offer Available • AdRun AI, ML, and HPC workloads on powerful cloud GPUs—without limits or wasted spend. Deploy GPUs in under a minute and pay by the second.
- Implementing a physics-informed DeepONet from scratch☆63Jul 9, 2023Updated 3 years ago
- A minimal implementation of Physics-Informed Neural Networks (PINNs) in PyTorch☆27Nov 15, 2023Updated 2 years ago
- Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."☆86Apr 24, 2025Updated last year
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data☆394Jul 14, 2023Updated 3 years ago
- ☆11Jan 7, 2025Updated last year
- ☆428Dec 3, 2022Updated 3 years ago
- Official repo for separable operator networks -- extreme-scale operator learning for parametric PDEs.☆41Nov 2, 2024Updated last year
- Encoding physics to learn reaction-diffusion processes☆112Aug 28, 2023Updated 3 years ago
- Code accompanying the manuscript "Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition m…☆16Sep 18, 2023Updated 2 years ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- Different neural operators for various numerical experiments☆24May 4, 2025Updated last year
- [ICLR24] A boundary-embedded neural operator that incorporates complex boundary shape and inhomogeneous boundary values☆33Dec 5, 2024Updated last year
- Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators☆839Jun 25, 2022Updated 4 years ago
- ☆57Oct 9, 2022Updated 3 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆169May 15, 2024Updated 2 years ago
- ☆14Apr 24, 2026Updated 4 months ago
- A comprehensive benchmark for machine learning multiphysics flows☆21Feb 3, 2026Updated 6 months ago
- ☆23Aug 14, 2025Updated last year
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems☆115Apr 15, 2022Updated 4 years ago
- 1-Click AI Models by DigitalOcean Gradient • AdDeploy popular AI models on DigitalOcean Gradient GPU virtual machines with just a single click. Zero configuration with optimized deployments.
- Physcial Informed Extreme Learning Machine(PIELM) method to solve PDEs, such as Possion problem☆18Dec 6, 2024Updated last year
- ☆12May 3, 2023Updated 3 years ago
- Code for training and inferring acoustic wave propagation in 3D☆47May 12, 2026Updated 3 months ago
- A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks☆106Oct 24, 2022Updated 3 years ago
- ☆48Sep 2, 2025Updated last year
- ☆33Oct 6, 2022Updated 3 years ago
- ☆25Jul 16, 2026Updated last month