marcosbenicio/pinns
Studies on Physics-Informed Neural Networks (PINNs) for solving problems governed by partial differential equations.
This helps researchers and engineers working with complex physical systems to model and understand their behavior. It takes the mathematical description of a system, like the Burgers' equation in fluid mechanics, along with its initial and boundary conditions, and produces a visual approximation of how a key variable (like velocity) evolves over space and time. This is for scientists, fluid dynamicists, and other domain experts who need to simulate and predict physical phenomena.
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Use this if you need to solve complex partial differential equations (PDEs) that describe physical systems, especially when traditional numerical methods are challenging or data is sparse.
Not ideal if you are looking for a simple, off-the-shelf simulator for basic fluid dynamics problems without needing to understand or implement the underlying neural network architecture.
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Jun 23, 2023
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