NeuralPDE.jl and NABLA-SciML
About NeuralPDE.jl
SciML/NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
This tool helps scientists and engineers solve complex partial differential equations (PDEs) that describe physical phenomena, even when traditional methods struggle. You input your differential equations and boundary conditions, and it outputs a highly accurate numerical solution, often faster and with greater flexibility than conventional techniques. It's designed for researchers, modelers, and simulation specialists who need to understand and predict behavior in systems governed by differential equations, without needing deep expertise in advanced numerical solvers.
About NABLA-SciML
jdtoscano94/NABLA-SciML
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
This project provides advanced machine learning tools to help researchers and scientists analyze complex physical systems that are difficult to study with traditional methods. It takes experimental data or mathematical descriptions of physical phenomena and produces predictions or insights into system behavior, such as fluid flows or turbulent convection. This is for computational scientists, engineers, and physicists researching intricate physical processes.
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