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cs.LG2024
Structural Constraints for Physics-augmented Learning
Simon Kuang, Xinfan Lin
When the physics is wrong, physics-informed machine learning becomes physics-misinformed machine learning. A powerful black-box model should not be able to conceal misconceived phy…
eess.SY2024
A New Type of Nonlinear Disturbance Rejection
Simon Kuang, Xinfan Lin
Asymptotic disturbance rejection (equivalently tracking) for nonlinear systems has been studied only in qualitative terms (the state is asymptotically stable under bounded disturba…
eess.SY2024
System Identification for Lithium-Ion Batteries with Nonlinear Coupled Electro-Thermal Dynamics via Bayesian Optimization
Hao Tu, Xinfan Lin, Yebin Wang +1
Essential to various practical applications of lithium-ion batteries is the availability of accurate equivalent circuit models. This paper presents a new coupled electro-thermal mo…