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0 results for “neural operators”
L-FNO: Lorentzian Fourier Neural Operator for Stochastic Event Dynamics
Researchers introduced L-FNO, a new stochastic neural operator architecture designed to model rare, bursty, self-exciting events in operational systems by integrating Lorentzian spectral kernels and likelihood-based training — improving event likelihood estimation, calibration, and rare-event detection over existing neural operator baselines.
Aug 17, 2026
Geometry-aware Incremental Neural Operator for Long-Horizon PDE prediction
Researchers introduced GeoIncNO, a new neural operator architecture designed to improve stability and accuracy in long-horizon autoregressive prediction of partial differential equations by structuring latent increments with geometric awareness and decoupling mean and fluctuation reconstruction.
Aug 13, 2026
Feature Interaction Modeling for Physics-Informed Neural Networks and Neural Operators
Researchers introduced feature interaction modules from factorization machines into physics-informed neural networks and neural operators to improve accuracy on parameterized PDEs with strong cross-variable dependencies, especially shock-dominated or discontinuous systems.
Aug 3, 2026