NeuralPDE.jl and XPINNs
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 XPINNs
AmeyaJagtap/XPINNs
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
When solving complex physics problems, this framework helps researchers and engineers model systems governed by nonlinear partial differential equations (PDEs), even those with intricate geometries or discontinuous behaviors. It takes your PDE problem definition and produces a trained neural network model that can predict system behavior more efficiently than standard methods. This tool is ideal for computational scientists, physicists, and engineers working on simulations and analyses where traditional PDE solvers struggle with complexity or computational cost.
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