The Neural Compiler converts symbolic programs into exact differentiable PyTorch modules for hybrid scientific machine learning, enabling precise encoding of known physics with few trainable parameters.
Lagrangian neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Unified generalization bounds for PINNs and VPINNs are derived by representing nonlinear differential operators via Taylor expansion and Koopman theory, showing high-rank networks generalize well while nonlinearity exponentially enlarges the bounds.
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The Neural Compiler: Program-to-Network Translation for Hybrid Scientific Machine Learning
The Neural Compiler converts symbolic programs into exact differentiable PyTorch modules for hybrid scientific machine learning, enabling precise encoding of known physics with few trainable parameters.
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Unified generalization analysis for physics informed neural networks
Unified generalization bounds for PINNs and VPINNs are derived by representing nonlinear differential operators via Taylor expansion and Koopman theory, showing high-rank networks generalize well while nonlinearity exponentially enlarges the bounds.