Physics guided machine learning using simplified theories

18 December 2020

Papers citing "Physics guided machine learning using simplified theories"

9 / 9 papers shown

Title
Sparse deep neural networks for modeling aluminum electrolysis dynamics E. Lundby Adil Rasheed I. Halvorsen J. Gravdahl 21 14 0 13 Sep 2022
Decentralized digital twins of complex dynamical systems Omer San Suraj Pawar Adil Rasheed AI4CE 38 11 0 07 Jul 2022
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
Supplementation of deep neural networks with simplified physics-based features to increase model prediction accuracy Nicholus R. Clinkinbeard Nicole N. Hashemi PINN AI4CE 35 0 0 14 Apr 2022
A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic Barbara Z Cunha C. Droz A. Zine Stéphane Foulard M. Ichchou AI4CE 29 84 0 13 Apr 2022
Image features of a splashing drop on a solid surface extracted using a feedforward neural network Jingzu Yee A. Yamanaka Yoshiyuki Tagawa(田川義之) 19 14 0 24 Jan 2022
Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution Omer San Adil Rasheed T. Kvamsdal 48 50 0 26 Mar 2021
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling Naoya Takeishi Alexandros Kalousis DRL AI4CE 24 54 0 25 Feb 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 273 5,660 0 05 Dec 2016