Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach

7 June 2022

Papers citing "Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach"

8 / 8 papers shown

Title
Physics guided neural networks for modelling of non-linear dynamics Haakon Robinson Suraj Pawar Adil Rasheed Omer San PINN AI4TS AI4CE 54 49 0 13 May 2022
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs) Michael Penwarden Shandian Zhe A. Narayan Robert M. Kirby 71 45 0 25 Jun 2021
Deep neural network enabled corrective source term approach to hybrid analysis and modeling Sindre Stenen Blakseth Adil Rasheed T. Kvamsdal Omer San 54 25 0 24 May 2021
Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution Omer San Adil Rasheed T. Kvamsdal 82 53 0 26 Mar 2021
Physics guided machine learning using simplified theories Suraj Pawar Omer San Burak Aksoylu Adil Rasheed T. Kvamsdal PINN AI4CE 134 107 0 18 Dec 2020
Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data Hao Xu Dongxiao Zhang Nanzhe Wang 70 34 0 24 Nov 2020
Data-Driven Discovery of Coarse-Grained Equations Joseph Bakarji D. Tartakovsky 52 32 0 30 Jan 2020
OptNet: Differentiable Optimization as a Layer in Neural Networks Brandon Amos J. Zico Kolter 158 963 0 01 Mar 2017