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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Muhammed Yuguda

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#graph neural networks Open access Aug 2026

pinn-serving: Derivative-Aware Serving of Physics-Informed Neural Networks

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 backends and quantisation. Includes evidence that output-space accuracy monitoring cannot detect physics degradation at serving time, and a matched comparison against a Crank-Nicolson solver.

Muhammed Yuguda, Abdullahi Muhammad Vatsa · 0 citations
#graph neural networks Open access Aug 2026

pinn-serving: Derivative-Aware Serving of Physics-Informed Neural Networks

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 backends and quantisation. Includes evidence that output-space accuracy monitoring cannot detect physics degradation at serving time, and a matched comparison against a Crank-Nicolson solver.

Muhammed Yuguda, Abdullahi Muhammad Vatsa · 0 citations
#graph neural networks Open access Aug 2026

pinn-serving: Derivative-Aware Serving of Physics-Informed Neural Networks

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 backends and quantisation. Includes evidence that output-space accuracy monitoring cannot detect physics degradation at serving time, and a matched comparison against a Crank-Nicolson solver.

Muhammed Yuguda, Abdullahi Muhammad Vatsa · 0 citations
#graph neural networks Open access Aug 2026

pinn-serving: Derivative-Aware Serving of Physics-Informed Neural Networks

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 backends and quantisation. Includes evidence that output-space accuracy monitoring cannot detect physics degradation at serving time, and a matched comparison against a Crank-Nicolson solver.

Muhammed Yuguda, Abdullahi Muhammad Vatsa · 0 citations

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