DedalusProject / dedalus
A flexible framework for solving PDEs with modern spectral methods.
☆543Updated this week
Alternatives and similar repositories for dedalus:
Users that are interested in dedalus are comparing it to the libraries listed below
- Python package for solving partial differential equations using finite differences.☆429Updated this week
- Spectral Navier Stokes (and similar) solvers in Python☆313Updated last year
- Differentiable Fluid Dynamics Package☆377Updated last month
- High performance computational platform in Python for the spectral Galerkin method☆210Updated 3 months ago
- Computational Fluid Dynamics in JAX☆800Updated last month
- Neural network based solvers for partial differential equations and inverse problems . Implementation of physics-informed neural networks…☆146Updated 2 months ago
- ☆411Updated last year
- Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs…☆362Updated 3 months ago
- Python package for numerical derivatives and partial differential equations in any number of dimensions.☆471Updated 2 months ago
- Automatic Differentiation Library for Computational and Mathematical Engineering☆295Updated last year
- Data-driven model reduction library with an emphasis on large scale parallelism and linear subspace methods☆216Updated 3 weeks ago
- Physics-Informed Neural networks for Advanced modeling☆446Updated this week
- A framework for hydrodynamics explorations and prototyping☆312Updated last week
- A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, i…☆732Updated 2 weeks ago
- Using graph network to solve PDEs☆376Updated last year
- ☆246Updated 2 years ago
- ☆191Updated 3 years ago
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations☆277Updated 2 years ago
- A code for fast, massively-parallel direct numerical simulations (DNS) of canonical flows☆220Updated 2 weeks ago
- Firedrake is an automated system for the portable solution of partial differential equations using the finite element method (FEM)☆541Updated this week
- Deep learning for Engineers - Physics Informed Deep Learning☆332Updated last year
- Code for "Learning data-driven discretizations for partial differential equations"☆167Updated 5 years ago
- Finite Volume simulation of the Kelvin-Helmholtz Instability☆134Updated last year
- TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).☆256Updated last year
- ☆322Updated 2 years ago
- Next generation FEniCS problem solving environment☆852Updated this week
- Hidden Fluid Mechanics☆314Updated 2 years ago
- Simple finite element assemblers☆552Updated last month
- ☆272Updated this week
- Curated list for ML in FM☆199Updated last week