A lightweight framework for building Physics-Informed Neural Networks (PINNs) with symbolic PDE definitions using SymPy and automatic differentiation in PyTorch. It provides flexible neural architectures, inverse parameter estimation, and automatic loss generation from PDEs, conditions, and data.
☆72Oct 9, 2026Updated 2 weeks ago
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