no code implementations • 23 Feb 2024 • Christian Moya, Amirhossein Mollaali, Zecheng Zhang, Lu Lu, Guang Lin
In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification (UQ) framework, to obtain confidence prediction intervals with coverage guarantees for Deep Operator Network (DeepONet) regression.
1 code implementation • 22 Jan 2024 • Haoyang Zheng, Wei Deng, Christian Moya, Guang Lin
Approximate Thompson sampling with Langevin Monte Carlo broadens its reach from Gaussian posterior sampling to encompass more general smooth posteriors.
no code implementations • 28 Nov 2023 • Zhihao Kong, Amirhossein Mollaali, Christian Moya, Na Lu, Guang Lin
This work redesigns MIONet, integrating Long Short Term Memory (LSTM) to learn neural operators from time-dependent data.
no code implementations • 7 Nov 2023 • Amirhossein Mollaali, Izzet Sahin, Iqrar Raza, Christian Moya, Guillermo Paniagua, Guang Lin
To address this challenge, this paper proposes a deep operator learning-based framework that requires a limited high-fidelity dataset for training.
no code implementations • 29 Oct 2023 • Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin, Hayden Schaeffer
Neural operators have been applied in various scientific fields, such as solving parametric partial differential equations, dynamical systems with control, and inverse problems.
no code implementations • 1 Jun 2023 • Izzet Sahin, Christian Moya, Amirhossein Mollaali, Guang Lina, Guillermo Paniagua
The datasets needed to train and test the proposed DeepONet framework were obtained by simulating a 2D rib-roughened internal cooling channel.
no code implementations • 29 Jan 2023 • Christian Moya, Guang Lin, Tianqiao Zhao, Meng Yue
This paper designs an Operator Learning framework to approximate the dynamic response of synchronous generators.
no code implementations • 21 Sep 2022 • Yixuan Sun, Christian Moya, Guang Lin, Meng Yue
This paper develops a Deep Graph Operator Network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e. g. the power grid or traffic) with an underlying sub-graph structure.
no code implementations • 15 Feb 2022 • Christian Moya, Shiqi Zhang, Meng Yue, Guang Lin
This paper proposes a new data-driven method for the reliable prediction of power system post-fault trajectories.
no code implementations • 3 Nov 2021 • Guang Lin, Christian Moya, Zecheng Zhang
To enable DeepONets training with noisy data, we propose using the Bayesian framework of replica-exchange Langevin diffusion.
no code implementations • 9 Sep 2021 • Christian Moya, Guang Lin
Deep learning-based surrogate modeling is becoming a promising approach for learning and simulating dynamical systems.