Zero-shot super-resolution is information-theoretically impossible for some simple operators but possible under Hölder smoothness of outputs, accompanied by generalization bounds.
Active learning for neural PDE solvers.CoRR, abs/2408.01536
4 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
STAP reduces training data costs for PDE surrogates by selectively acquiring key time steps per trajectory instead of full simulations.
Physics-based active learning using PDE residuals improves data efficiency for neural operator training on Burgers and Navier-Stokes equations while adding a physics inductive bias.
A differentiable neural operator learns the mapping from granular microstructure configurations to failure envelopes, with physics-informed convexity enforcement and active learning for efficient training.
citing papers explorer
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Is Zero-Shot Super-Resolution Possible in Operator Learning?
Zero-shot super-resolution is information-theoretically impossible for some simple operators but possible under Hölder smoothness of outputs, accompanied by generalization bounds.
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Active Learning with Selective Time-Step Acquisition for PDEs
STAP reduces training data costs for PDE surrogates by selectively acquiring key time steps per trajectory instead of full simulations.
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Data-Efficient Neural Operator Training via Physics-Based Active Learning
Physics-based active learning using PDE residuals improves data efficiency for neural operator training on Burgers and Navier-Stokes equations while adding a physics inductive bias.
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Neural Operator Representation of Granular Micromechanics-based Failure Envelope
A differentiable neural operator learns the mapping from granular microstructure configurations to failure envelopes, with physics-informed convexity enforcement and active learning for efficient training.