Physics-informed machine learning model for battery state of health prognostics using partial charging segments
The accurate and efficient estimation of battery state-of-health (SoH) is an ever-significant
issue for applications of lithium-ion batteries (LIBs). Physics-of-failure (PoF) and machine …
issue for applications of lithium-ion batteries (LIBs). Physics-of-failure (PoF) and machine …
Hall-effect sensor design with physics-informed Gaussian process modeling
Magnetic field sensor devices have been widely used to track changes in magnetic flux
concentration, and the Hall sensors are promising in many engineering applications. Design …
concentration, and the Hall sensors are promising in many engineering applications. Design …
Numerical modeling on the delamination-induced capacity degradation of silicon anode
Silicon is a promising candidate for the negative electrode in lithium-ion battery. However,
silicon-based electrodes experience large volume changes during the lithiation-delithiation …
silicon-based electrodes experience large volume changes during the lithiation-delithiation …
An enhanced squared exponential kernel with Manhattan similarity measure for high dimensional Gaussian process models
Abstract The Gaussian Process (GP) model has become one of the most popular methods
and exhibits superior performance among surrogate models in many engineering design …
and exhibits superior performance among surrogate models in many engineering design …
Hall Effect Sensor Design Optimization With Multi-Physics Informed Gaussian Process Modeling
Magnetic field sensor devices have been widely used to track changes in magnetic flux
concentration, and the Hall sensors are promising in many engineering applications. Design …
concentration, and the Hall sensors are promising in many engineering applications. Design …
Machine learning assisted design for active cathode materials
The traditional way of designing materials, including experimental measurement and
computational simulation, are not efficient. Machine learning is considered a promising …
computational simulation, are not efficient. Machine learning is considered a promising …
Uncertainty Quantification Analysis on Silicon Electrodeposition Process Via Numerical Simulation Methods
Silicon is one of the commonly used semiconductors for various industrial applications.
Traditional silicon synthesis methods are often expensive and cannot meet the continuously …
Traditional silicon synthesis methods are often expensive and cannot meet the continuously …