A serving harness and benchmark suite for physics-informed neural networks (PINNs). Introduces derivative-graph baking, which differentiates a tanh MLP symbolically offline and emits the closed-form derivative recursion as a forward-only program, making derivative-valued queries compatible with standard inference backe...
Muhammed Yuguda, Abdullahi Muhammad Vatsa· Zenodo (CERN European Organi...· 0 citations
A serving harness and benchmark suite for physics-informed neural networks (PINNs). Introduces derivative-graph baking, which differentiates a tanh MLP symbolically offline and emits the closed-form derivative recursion as a forward-only program, making derivative-valued queries compatible with standard inference backe...
Muhammed Yuguda, Abdullahi Muhammad Vatsa· Zenodo (CERN European Organi...· 0 citations
A serving harness and benchmark suite for physics-informed neural networks (PINNs). Introduces derivative-graph baking, which differentiates a tanh MLP symbolically offline and emits the closed-form derivative recursion as a forward-only program, making derivative-valued queries compatible with standard inference backe...
Muhammed Yuguda, Abdullahi Muhammad Vatsa· Zenodo (CERN European Organi...· 0 citations
A serving harness and benchmark suite for physics-informed neural networks (PINNs). Introduces derivative-graph baking, which differentiates a tanh MLP symbolically offline and emits the closed-form derivative recursion as a forward-only program, making derivative-valued queries compatible with standard inference backe...
Muhammed Yuguda, Abdullahi Muhammad Vatsa· Zenodo (CERN European Organi...· 0 citations
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