Physics-informed neural networks with hard constraints for inverse design
☆162Nov 21, 2021Updated 4 years ago
Alternatives and similar repositories for hpinn
Users that are interested in hpinn are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems☆115Apr 15, 2022Updated 4 years ago
- A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks☆106Oct 24, 2022Updated 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
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆62Jan 25, 2022Updated 4 years ago
- A library for scientific machine learning and physics-informed learning☆4,409Aug 18, 2026Updated 2 weeks 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.
- Tutorials on deep learning, Python, and dissipative particle dynamics☆225Mar 21, 2026Updated 5 months ago
- ☆428Dec 3, 2022Updated 3 years ago
- This is the source code for our paper "Towards high-accuracy deep learning inference of compressible turbulent flows over aerofoils"☆34Mar 11, 2024Updated 2 years ago
- ☆174Jun 27, 2022Updated 4 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
- ☆13Nov 20, 2019Updated 6 years ago
- ☆27Oct 15, 2020Updated 5 years ago
- ☆63Jul 24, 2019Updated 7 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.
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆91Jan 24, 2021Updated 5 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆95Aug 26, 2025Updated last year
- ☆57Oct 9, 2022Updated 3 years ago
- Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]☆299Oct 12, 2021Updated 4 years ago
- ☆272Oct 14, 2021Updated 4 years ago
- Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels -- param…☆25Aug 2, 2021Updated 5 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Jul 23, 2020Updated 6 years ago
- ☆37Feb 13, 2022Updated 4 years ago
- Must-read Papers on Physics-Informed Neural Networks.☆1,537Dec 8, 2023Updated 2 years ago
- Wordpress hosting with auto-scaling - Free Trial Offer • AdFully Managed hosting for WordPress and WooCommerce businesses that need reliable, auto-scalable performance. Cloudways SafeUpdates now available.
- 🌌 Applications of Physics-Informed ML: A collection of notebooks from my Masters research, exploring how machine learning can solve scie…☆12Apr 29, 2026Updated 4 months ago
- Python implementation of a 2D Smoothed Particle Hydrodynamics (SPH) simulation for simulating fluid behavior in a dam break scenario.☆12Aug 10, 2023Updated 3 years ago
- ☆16Oct 29, 2023Updated 2 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆155Oct 18, 2019Updated 6 years ago
- ☆17Jun 7, 2025Updated last year
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆84Aug 4, 2023Updated 3 years ago
- Original implementation of fast PINN optimization with RBA weights☆78Sep 11, 2025Updated 11 months ago
- MIONet: Learning multiple-input operators via tensor product☆47Nov 13, 2022Updated 3 years ago
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆44Feb 1, 2023Updated 3 years ago
- AI Agents on DigitalOcean Gradient AI Platform • AdBuild production-ready AI agents using customizable tools or access multiple LLMs through a single endpoint. Create custom knowledge bases or connect external data.
- Boosting the training of physics informed neural networks with transfer learning☆27Jun 5, 2021Updated 5 years ago
- Numerical assessments of a nonintrusive surrogate model based on recurrent neural networks and proper orthogonal decomposition: Rayleigh …☆10Dec 2, 2022Updated 3 years ago
- Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations☆6,128Feb 11, 2026Updated 6 months ago
- Enhancing the convergence speed by 2x and improving the training success of Physics-Informed Neural Networks (PINNs).☆13Oct 14, 2024Updated last year
- Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators☆842Jun 25, 2022Updated 4 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆90Jul 15, 2022Updated 4 years ago
- Code accompanying "Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks", Maddu et al., 2021☆14Nov 3, 2021Updated 4 years ago