Shypd.aiThe largest open index of AI toolsFBPINNs is a Research & Education tool that solves forward and inverse problems related to partial differential equations (PDEs). It uses finite basis physics-informed neural networks (FBPINNs) and is rewritten in JAX for significantly faster performance.
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FBPINNs provides a robust framework for solving complex forward and inverse problems involving partial differential equations (PDEs). It leverages finite basis physics-informed neural networks (FBPINNs), which combine the strengths of PINNs with domain decomposition, individual subdomain normalization, and flexible training schedules. This approach significantly outperforms traditional PINNs in accuracy and computational efficiency, especially for problems with high frequencies or multi-scale solutions. The library has been re-engineered in JAX, enabling 10-1000X faster execution by parallelizing subdomain computations. Users can define custom problem domains, PDEs, domain decompositions, and neural network architectures, making it highly flexible for various scientific computing tasks. It also supports solving inverse problems and integrating arbitrary boundary/data constraints.
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