Neural operators for accelerating scientific simulations and design
Scientific discovery and engineering design are currently limited by the time and cost of
physical experiments. Numerical simulations are an alternative approach but are usually …
physical experiments. Numerical simulations are an alternative approach but are usually …
Machine learning-guided protein engineering
Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …
machine learning methods. These methods leverage existing experimental and simulation …
Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods
H Wang, B Li, J Gong, FZ Xuan - Engineering Fracture Mechanics, 2023 - Elsevier
Fatigue life prediction is critical for ensuring the safe service and the structural integrity of
mechanical structures. Although data-driven approaches have been proven effective in …
mechanical structures. Although data-driven approaches have been proven effective in …
Machine learning-assisted low-dimensional electrocatalysts design for hydrogen evolution reaction
J Li, N Wu, J Zhang, HH Wu, K Pan, Y Wang, G Liu… - Nano-Micro Letters, 2023 - Springer
Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …
Nevertheless, the conventional" trial and error" method for producing advanced …
[HTML][HTML] Feedback on a shared big dataset for intelligent TBM Part I: Feature extraction and machine learning methods
Due to complexity, the oil and gas industry employs various sensors to collect data for
analysis to maintain the safety and integrity of pipelines and associated infrastructure. There …
analysis to maintain the safety and integrity of pipelines and associated infrastructure. There …