Amirhossein Mollaali

According to our database, Amirhossein Mollaali authored at least 7 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

pADAM: A Plug-and-Play All-in-One Diffusion Architecture for Multi-Physics Learning.

Amirhossein Mollaali, Bongseok Kim, Christian Moya, Guang Lin

CoRR, March, 2026

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

CoRR, April, 2025

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

Neural Networks, 2025

2024

Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks.

Christian Moya, Amirhossein Mollaali, Zecheng Zhang, Lu Lu, Guang Lin

CoRR, 2024

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

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

CoRR, 2023

Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles.

Izzet Sahin, Christian Moya, Amirhossein Mollaali, Guang Lin, Guillermo Paniagua

CoRR, 2023