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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Use this if you need to solve complex partial differential equations and require a method that can handle arbitrary domain geometries, reduce computation time through parallelization, and adapt to varying solution complexities across different regions.
Not ideal if your problems are simple and can be solved efficiently with conventional numerical methods or if you prefer a single-network approach over a domain decomposition strategy.
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MIT
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Last pushed
Feb 01, 2023
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