Robust physics discovery via supervised and unsupervised pattern recognition using the Euler characteristic

DOI10.1016/j.cma.2022.115110OpenAlexW3210000009MaRDI QIDQ2145118

Nan Xu, Zhi-Ming Zhang, Yong-Ming Liu

Publication date: 17 June 2022

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2110.13610

zbMATH Keywords

pattern recognitionEuler characteristicpartial differential equationphysics discovery

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

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Cites Work

• Unnamed Item
• Fréchet means for distributions of persistence diagrams
• Stability of persistence diagrams
• DLGA-PDE: discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
• A robust framework for identification of PDEs from noisy data
• Data-driven identification of 2D partial differential equations using extracted physical features
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Robust data-driven discovery of governing physical laws with error bars
• Barcodes: The persistent topology of data