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A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems

Journal Article · · IEEE Signal Processing Magazine

Not provided.

Research Organization:
Brown Univ., Providence, RI (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
SC0019453
OSTI ID:
1980447
Journal Information:
IEEE Signal Processing Magazine, Vol. 39, Issue 1; ISSN 1053-5888
Publisher:
IEEE
Country of Publication:
United States
Language:
English

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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations journal February 2019
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks journal March 2020
Physics-Informed Neural Network for Ultrasound Nondestructive Quantification of Surface Breaking Cracks journal August 2020
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Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks
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