Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)

Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)

25 June 2021

Michael Penwarden

Robert M. Kirby

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

16 / 16 papers shown

Title
Equilibrium Conserving Neural Operators for Super-Resolution Learning Vivek Oommen Andreas E. Robertson Daniel Diaz Coleman Alleman Zhen Zhang Anthony D. Rollett George Karniadakis Rémi Dingreville 33 1 0 18 Apr 2025
Graph Laplacian-based Bayesian Multi-fidelity Modeling Orazio Pinti Jeremy M. Budd Franca Hoffmann Assad A. Oberai 35 1 0 12 Sep 2024
Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems Amanda A. Howard Bruno Jacob Sarah H. Murphy Alexander Heinlein P. Stinis AI4CE 41 26 0 28 Jun 2024
Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust Metrics Michael Penwarden H. Owhadi Robert M. Kirby AI4CE 22 1 0 16 Feb 2024
Stacked networks improve physics-informed training: applications to neural networks and deep operator networks Amanda A. Howard Sarah H. Murphy Shady E. Ahmed P. Stinis AI4CE 55 18 0 11 Nov 2023
A few-shot graph Laplacian-based approach for improving the accuracy of low-fidelity data Orazio Pinti Assad A. Oberai 20 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 29 28 0 03 Mar 2023
An iterative multi-fidelity approach for model order reduction of multi-dimensional input parametric PDE systems Manisha Chetry D. Borzacchiello Lucas Lestandi Luisa Rocha-Da-Silva 12 0 0 23 Jan 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
Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems Amanda A. Howard M. Perego G. Karniadakis P. Stinis AI4CE 36 53 0 19 Apr 2022
AutoIP: A United Framework to Integrate Physics into Gaussian Processes D. Long Zhilin Wang Aditi S. Krishnapriyan Robert M. Kirby Shandian Zhe Michael W. Mahoney AI4CE 26 14 0 24 Feb 2022
State-of-the-Art Review of Design of Experiments for Physics-Informed Deep Learning Sourav Das S. Tesfamariam PINN AI4CE 11 19 0 13 Feb 2022
Cost-effective Framework for Gradual Domain Adaptation with Multifidelity Shogo Sagawa H. Hino CLL 29 6 0 09 Feb 2022
A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs Michael Penwarden Shandian Zhe A. Narayan Robert M. Kirby PINN AI4CE 25 39 0 26 Oct 2021
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 183 760 0 13 Mar 2020
Benefits of depth in neural networks Matus Telgarsky 148 602 0 14 Feb 2016