Battery degradation, caused by multiple coupled degradation mechanisms, severely affects
the safety and sustainability of a battery management system (BMS). The battery state of …

To quantify uncertainty, conformal prediction methods are gaining continuously more
interest and have already been successfully applied to various domains. However, they are …

Conventional empirical equations for soil properties prediction tend to be site-specific,
exhibiting poor reliability and accuracy. Meanwhile, alternative data-driven methods require …

Regression models are of fundamental importance in explicitly explaining the response
variable in terms of covariates. However, point predictions of these models limit them from …

A sparse binary neural network is a discrete-time recurrent neural network characterized by
local binary connections and signum-type neuron models. The dynamics is described by an …

Tensor Networks (TNs) have recently been used to speed up kernel machines by
constraining the model weights, yielding exponential computational and storage savings. In …

It has previously been shown that prior physics knowledge can be incorporated into the
structure of an artificial neural network via neural activation functions based on (i) the …

State of health (SOH) estimation of Lithium-ion battery plays a key role in battery
management system, as it characterizes the battery's health state and ensures safe and …

In practice, a purely data-driven approach for building a generic 2D or 3D stress-strain
relationship without introducing physical constraints (assumptions) is rather difficult to be …