Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles. - Izzet Sahin; Christian Moya; Amirhossein Mollaali; Guang Lin 001; Guillermo Paniagua (CoRR, 2023)

A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients. - Amirhossein Mollaali; Izzet Sahin; Iqrar Raza; Christian Moya; Guillermo Paniagua; Guang Lin 001 (CoRR, 2023)

B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions. - Zhihao Kong; Amirhossein Mollaali; Christian Moya; Na Lu; Guang Lin 001 (CoRR, 2023)

Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks. - Christian Moya; Amirhossein Mollaali; Zecheng Zhang; Lu Lu; Guang Lin 001 (CoRR, 2024)

Conformalized prediction of post-fault voltage trajectories using pre-trained and finetuned attention-driven neural operators. - Amirhossein Mollaali; Gabriel Zufferey; Gonzalo Constante-Flores; Christian Moya; Can Li; Meng Yue; Guang Lin 001 (Neural Networks, 2025)

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning. - Amirhossein Mollaali; Christian B. Moya; Amanda A. Howard; Alexander Heinlein; Panos Stinis; Guang Lin 001 (CoRR, 2025)