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IDRLnet is a machine learning library on top of PyTorch. Use IDRLnet if you need a machine learning library that solves both forward and inverse differential equations via physics-informed neural networks (PINN). IDRLnet is a flexible framework inspired by Nvidia Simnet.
IDRLnet supports
- complex domain geometries without mesh generation. Provided geometries include interval, triangle, rectangle, polygon, circle, sphere... Other geometries can be constructed using three boolean operations: union, difference, and intersection;
- sampling in the interior of the defined geometry or on the boundary with given conditions.
- enables the user code to be structured. Data sources, operations, constraints are all represented by
Node
. The graph will be automatically constructed via label symbols of each node. Getting rid of the explicit construction via explicit expressions, users model problems more naturally. - solving variational minimization problem;
- solving integral differential equation;
- adaptive resampling;
- recover unknown parameter of PDEs from noisy measurement data.
If you are looking for usage of a specific function, class or method, please refer to the following part.
modules/modules
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modindex
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