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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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
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An end-to-end differentiable co-optimization method uses implicit neural representations of geometry together with a JAX multiphysics solver to jointly tune shape, material state, and boundary conditions over transient rollouts, shown on a hamburger-cooking benchmark.
GICON combines graph message passing with example-aware positional encoding to enable in-context operator learning that outperforms classical operator learning on air quality prediction tasks across regions.
citing papers explorer
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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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Differentiable Multiphysics Co-Optimization via Implicit Neural Representations: A Transient Hamburger-Cooking Benchmark
An end-to-end differentiable co-optimization method uses implicit neural representations of geometry together with a JAX multiphysics solver to jointly tune shape, material state, and boundary conditions over transient rollouts, shown on a hamburger-cooking benchmark.
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Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction
GICON combines graph message passing with example-aware positional encoding to enable in-context operator learning that outperforms classical operator learning on air quality prediction tasks across regions.