Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)

25 June 2021

Papers citing "Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)"

7 / 7 papers shown

Title
Graph Laplacian-based Bayesian Multi-fidelity Modeling Orazio Pinti Jeremy M. Budd Franca Hoffmann Assad A. Oberai 33 1 0 12 Sep 2024
A few-shot graph Laplacian-based approach for improving the accuracy of low-fidelity data Orazio Pinti Assad A. Oberai 18 0 0 29 Mar 2023
Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook Xuan Di Rongye Shi Zhaobin Mo Yongjie Fu PINN AI4TS AI4CE 26 28 0 03 Mar 2023
Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach Sindre Stenen Blakseth Adil Rasheed T. Kvamsdal Omer San AI4CE 26 31 0 07 Jun 2022
AutoIP: A United Framework to Integrate Physics into Gaussian Processes D. Long Zhongqi Wang Aditi S. Krishnapriyan Robert M. Kirby Shandian Zhe Michael W. Mahoney AI4CE 24 14 0 24 Feb 2022
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 183 759 0 13 Mar 2020
Benefits of depth in neural networks Matus Telgarsky 142 602 0 14 Feb 2016