ZNO is a causal neural operator for discrete-time dynamics that parameterizes stable low-rank rational filters in the z-plane and shows advantages on systems with poles near the unit circle.
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AD-RaNN learns an effective low-dimensional sampling distribution for hidden parameters in randomized neural networks by optimizing a vector p via PDE-driven or data-driven adaptation and a two-stage least-squares procedure, improving accuracy on benchmark PDE problems.
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.
AMORE develops an adaptive multi-output DeepONet with custom losses, partition-of-unity trunk, and invertible/softmax mass-fraction maps to surrogate stiff kinetics on syngas (12 states) and GRI-Mech (24 states).
Multiple Neural Operators achieve near-optimal approximation and generalization rates for multi-task operator learning, matching single-task scaling laws and performing similarly to a multi-task DeepONet extension.
The paper surveys AI surrogates including PINNs, neural operators, and hybrid generative models as ways to reach high-Re and high-S MHD regimes beyond direct numerical simulation.
citing papers explorer
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ZNO: Stable Rational Neural Operators in the Z-Domain for Discrete-Time Dynamics
ZNO is a causal neural operator for discrete-time dynamics that parameterizes stable low-rank rational filters in the z-plane and shows advantages on systems with poles near the unit circle.
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Adaptive-Distribution Randomized Neural Networks for PDEs: A Low-Dimensional Distribution-Learning Framework
AD-RaNN learns an effective low-dimensional sampling distribution for hidden parameters in randomized neural networks by optimizing a vector p via PDE-driven or data-driven adaptation and a two-stage least-squares procedure, improving accuracy on benchmark PDE problems.
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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.
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AMORE: Adaptive Multi-Output Operator Network for Stiff Chemical Kinetics
AMORE develops an adaptive multi-output DeepONet with custom losses, partition-of-unity trunk, and invertible/softmax mass-fraction maps to surrogate stiff kinetics on syngas (12 states) and GRI-Mech (24 states).
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Multiple Neural Operators Achieve Near-Optimal Rates for Multi-Task Learning
Multiple Neural Operators achieve near-optimal approximation and generalization rates for multi-task operator learning, matching single-task scaling laws and performing similarly to a multi-task DeepONet extension.
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Magnetohydrodynamics Simulations
The paper surveys AI surrogates including PINNs, neural operators, and hybrid generative models as ways to reach high-Re and high-S MHD regimes beyond direct numerical simulation.