Probabilistic and Physics-Informed Machine Learning for Predictive Maintenance with Time Series Data-Reference-Cited by-同舟云学术

Probabilistic and Physics-Informed Machine Learning for Predictive Maintenance with Time Series Data

Published:2023-04-17 Issue: Volume: Page:
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Container-title:2023 24th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)
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Author:

Vu Phan-Anh¹,Aldea Emanuel²,Bouarroudj Mounira³,Hégarat-Mascle Sylvie Le²

Affiliation:

1. Grenoble Alpes University,CEA LIST, CNRS, Grenoble INP, GIPSA-Lab,Grenoble,France,38000

2. Paris-Saclay University,CNRS, SATIE Laboratory,Gif-sur-Yvette,France,91190

3. Paris EST Créteil University,CNRS, SATIE Laboratory,Gif-sur-Yvette,France,91190

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10100701/10100725/10100781.pdf?arnumber=10100781

Reference26 articles.

1. Optimally weighted loss functions for solving PDEs with Neural Networks

2. Physics informed deep learning (part ii): Data-driven discovery of nonlinear partial differential equations;raissi;arXiv preprint arXiv 1711 10566,2017

3. A Critical Analysis of Crack Propagation Laws

4. Inverse Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks

5. Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations;raissi;arXiv preprint arXiv 1711 10871,2017

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