Hidden Fluid Mechanics
☆372Jan 30, 2023Updated 3 years ago
Alternatives and similar repositories for HFM
Users that are interested in HFM are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- ☆20May 29, 2021Updated 5 years ago
- Deep Learning of Vortex Induced Vibrations☆103Feb 21, 2020Updated 6 years ago
- Deep Learning of Turbulent Scalar Mixing☆19Oct 6, 2019Updated 7 years ago
- Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations☆6,242Feb 11, 2026Updated 8 months ago
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations☆294Jul 30, 2022Updated 4 years ago
- Managed Database hosting by DigitalOcean • AdPostgreSQL, MySQL, MongoDB, Kafka, Valkey, and OpenSearch available. Automatically scale up storage and focus on building your apps.
- Tutorial on a number of topics in Deep Learning☆38Feb 20, 2020Updated 6 years ago
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆269Feb 1, 2023Updated 3 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆154Feb 20, 2020Updated 6 years ago
- ☆274Oct 14, 2021Updated 5 years ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆82Feb 1, 2023Updated 3 years ago
- ☆282Nov 4, 2022Updated 4 years ago
- Backpropagation in Python, C++, and Cuda☆47Feb 17, 2020Updated 6 years ago
- Physics-informed neural network for solving fluid dynamics problems☆294Jan 28, 2021Updated 5 years ago
- A library for scientific machine learning and physics-informed learning☆4,497Updated this week
- 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.
- Code for "Learning data-driven discretizations for partial differential equations"☆166Aug 19, 2025Updated last year
- XPINN code written in TensorFlow 2☆28Feb 1, 2023Updated 3 years ago
- TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).☆268Nov 30, 2023Updated 2 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆71May 26, 2020Updated 6 years ago
- A framework for fluid flow (Reynolds-averaged Navier Stokes) predictions with deep learning☆354Nov 15, 2022Updated 3 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆91Jan 24, 2021Updated 5 years ago
- Hidden Fluid Mechanics in PyTorch☆21Nov 6, 2023Updated 3 years ago
- ☆66Mar 22, 2023Updated 3 years ago
- Deep learning framework for model reduction of dynamical systems☆21Dec 31, 2020Updated 5 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.
- ☆99Feb 5, 2025Updated last year
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems☆115Apr 15, 2022Updated 4 years ago
- Hidden Fluid Mechanics☆14Apr 10, 2022Updated 4 years ago
- ☆433Dec 3, 2022Updated 3 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆100Aug 17, 2023Updated 3 years ago
- Computational Fluid Dynamics in JAX☆969Oct 25, 2026Updated last week
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆97May 11, 2022Updated 4 years ago
- ☆59Mar 26, 2025Updated last year
- ☆222Feb 16, 2024Updated 2 years ago
- GPU virtual machines on DigitalOcean Gradient AI • AdGet to production fast with high-performance AMD and NVIDIA GPUs you can spin up in seconds. The definition of operational simplicity.
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems☆66Feb 20, 2020Updated 6 years ago
- Must-read Papers on Physics-Informed Neural Networks.☆1,558Dec 8, 2023Updated 2 years ago
- DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks☆336Jan 23, 2024Updated 2 years ago
- Examples of how to use machine learning algorithms in computational fluid dynamics.☆273Aug 26, 2022Updated 4 years ago
- Data-driven Reynolds stress modeling with physics-informed machine learning☆100Mar 21, 2019Updated 7 years ago
- Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube proble…☆232Feb 21, 2023Updated 3 years ago
- Physics-informed neural networks with hard constraints for inverse design☆166Nov 21, 2021Updated 4 years ago