MIDPhyNet: Memorized infusion of decomposed physics in neural networks to model dynamic systems

Author:

Zhang Zhibo,Rai Rahul,Chowdhury Souma,Doermann David

Funder

Defense Advanced Research Projects Agency

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference48 articles.

1. A hybrid approach for fusing physics and data for failure prediction;Pillai;Int. J. Prognostics Health Manage.,2016

2. X. Jia, A. Karpatne, J. Willard, M. Steinbach, J. Read, P.C. Hanson, H.A. Dugan, V. Kumar, Physics guided recurrent neural networks for modeling dynamical systems: Application to monitoring water temperature and quality in lakes, arXiv preprint arXiv:1810.02880.

3. R. Rai, C.K. Sahu, Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus, IEEE Access.

4. Multi-step-ahead prediction using dynamic recurrent neural networks;Parlos;Neural Networks,2000

5. Learning to segment object candidates, in;Pinheiro;Adv. Neural Inform. Processing Syst,2015

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