10 citations · 10 across the 6 of their papers we have counts for
4 papers · 1 filter
Physics-Informed Extreme Learning Machine (PIELM): Opportunities and Challenges
He Yang, Fei Ren, Francesco Calabro +3
We are delighted to see the recent development of physics-informed extreme learning machine (PIELM) for its higher computational efficiency and accuracy compared to other physics-i…
A Rapid Physics-Informed Machine Learning Framework Based on Extreme Learning Machine for Inverse Stefan Problems
Pei-Zhi Zhuang, Ming-Yue Yang, Fei Ren +2
The inverse Stefan problem, as a typical phase-change problem with moving boundaries, finds extensive applications in science and engineering. Recent years have seen the applicatio…
General Fourier Feature Physics-Informed Extreme Learning Machine (GFF-PIELM) for High-Frequency PDEs
Fei Ren, Sifan Wang, Pei-Zhi Zhuang +2
Conventional physics-informed extreme learning machine (PIELM) often faces challenges in solving partial differential equations (PDEs) involving high-frequency and variable-frequen…
Physics-Informed Extreme Learning Machine (PIELM) for Tunnelling-Induced Soil-Pile Interactions
Fu-Chen Guo, Pei-Zhi Zhuang, Fei Ren +2
Physics-informed machine learning has been a promising data-driven and physics-informed approach in geotechnical engineering. This study proposes a physics-informed extreme learnin…