A transformer-enhanced neural operator (TNO) predicted transonic compressor blade performance with lower error than state-of-the-art deep learning operators FNO and DeepONet, according to an arXiv pap…
Researchers proposed Physics-Informed Conformal Prediction (PI-CP), a framework that embeds PDE residuals into the nonconformity score of split conformal prediction to give neural operators distributi…
A controlled study of five DeepONet variants with distinct attention mechanisms finds that per-sensor tokenization with cross-attention reduces mean relative L2 error by factors of 2.4–28.0 across all…
Researchers introduced a data-poisoning attack called cross-parameter relinking that creates wrong-physics backdoors in neural PDE operators, making triggered inputs output physically plausible but in…
Researchers proposed the Physics Informed Kernel Function Neural Operator (PIKFNO), an interpretable neural operator that incorporates physics-informed kernel functions derived from governing equation…
A new arXiv preprint (arXiv:2608.06428v1) extends Topological DeepONets to use continuous linear functionals from the continuous dual of Hausdorff locally convex spaces, enabling fixed and adaptive fu…
Researchers introduced NOTES, a framework combining neural operators with evolutionary strategy, achieving over 95% efficiency in nanophotonic beam-deflector design by reducing dimensionality from 256…
Researchers proposed a scalable operator-learning framework, KL-DNN, for large-scale PDE problems, achieving 1.1 psi RMSE for pressure and 0.0146 for CO2 saturation in geological carbon storage simula…