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PINNForge: Execution-Grounded Evolutionary Design of Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) require coordinated choices across representation, architecture, sampling, constraints, loss construction, and optimization, yet effective configurations can vary substantially across partial differential equations (PDEs). Existing automated design methods se
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