Incompressible Navier-Stokes solver
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Updated
Jul 16, 2024 - Julia
Incompressible Navier-Stokes solver
Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.
A Julia package to perform Bifurcation Analysis
A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality
A Julia library for working with curvilinear grids
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Solve Fractional Differential Equations using high performance numerical methods
1D and 2D Wave simulation from scratch
Solve non-linear HJB equations.
An array type for MPI halo data exchange in Julia
Automatic Finite Difference PDE solving with Julia SciML
Taylor-mode automatic differentiation for higher-order derivatives
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language
Adaptive P/ODE numerics with Grassmann element TensorField assembly
Computes the boundary crossing probability for a general diffusion process and time-dependent boundary.
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