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STCO: Conditional Neural Operators for Time-Dependent PDEs

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1 SIGNALFIRST DETECTED 24 August 2026
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24 Aug
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13 Aug
Geometry-aware Incremental Neural Operator for Long-Horizon PDE prediction

arXiv:2608.11237v1 Announce Type: new Abstract: Neural operators have shown strong potential for learning solution operators of partial differential equations (PDEs). However,

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26 Mar
UniFluids: Unified Neural Operator Learning with Conditional Flow-matching

arXiv:2603.22309v1 Announce Type: cross Abstract: Partial differential equation (PDE) simulation holds extensive significance in scientific research. Currently, the integration of

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25 Aug
One-Step Evolution for Long-Time Extrapolation: An Error-Bound-Informed and Prior-Guided Neural Residual Framework for Autonomous PDEs

arXiv:2608.22026v1 Announce Type: new Abstract: Accurate simulation of the long-time evolution of systems governed by partial differential equations (PDEs) is central to

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20 Mar
Towards Efficient and Stable Ocean State Forecasting: A Continuous-Time Koopman Approach

arXiv:2603.05560v2 Announce Type: replace-cross Abstract: We investigate the Continuous-Time Koopman Autoencoder (CT-KAE) as a lightweight surrogate model for long-horizon ocean

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