Amirhossein Mollaali
Physics and Astronomy · Purdue University West Lafayette
Publications
16
Citations
58
Est. group size
~4
Recurring co-author estimate
Active years
7
Publishing since 2020
Amirhossein Mollaali works on scientific machine learning, developing neural network methods such as deep operator networks, Kolmogorov-Arnold Networks, and diffusion models to simulate and predict physical systems (like heat transfer and cooling systems). A recurring focus is uncertainty quantification—building methods that not only make predictions but also provide reliable estimates of how confident those predictions should be. Earlier work also touched on predicting equipment failure (bearing prognostics) using data-driven models.
Publication output was minimal or absent before 2020, then grew notably starting in 2023, with continued steady activity through 2026.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Conformalized-KANs: uncertainty quantification with coverage guarantees for Kolmogorov–Arnold Networks (KANs) in scientific machine learning
Machine Learning Science and Technology · 2026
- pADAM: A Plug-and-Play All-in-One Diffusion Architecture for Multi-Physics Learning
arXiv (Cornell University) · 2026
- pADAM: A Plug-and-Play All-in-One Diffusion Architecture for Multi-Physics Learning
arXiv (Cornell University) · 2026
- Conformalized-DeepONet: A distribution-free framework for uncertainty quantification in deep operator networks
Physica D Nonlinear Phenomena · 2024
- Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
arXiv (Cornell University) · 2024
- Deep Operator Learning-Based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles
SSRN Electronic Journal · 2023
- Deep operator learning-based surrogate models with uncertainty quantification for optimizing internal cooling channel rib profiles
International Journal of Heat and Mass Transfer · 2023
- Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles
arXiv (Cornell University) · 2023
- B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions
arXiv (Cornell University) · 2023
- A New Methodology to Deal with the Multi-phase Degradation in Rolling Element Bearing Prognostics
Smart innovation, systems and technologies · 2020
- Investigation on the effects of measurement and temporal uncertainties on rolling element bearings prognostics
2020
- arXiv (Cornell University)×7
- SSRN Electronic Journal×2
- International Journal of Heat and Mass Transfer×1
- Physica D Nonlinear Phenomena×1
- Machine Learning Science and Technology×1
- Yuezhu Xu
Physics and Astronomy · Purdue University West Lafayette
- Debdipta Goswami
Physics and Astronomy · The Ohio State University
- Naxian Ni
Physics and Astronomy · Purdue University West Lafayette
- Xihaier Luo
Physics and Astronomy · The Ohio State University
- Min Liu
Physics and Astronomy · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile