Transportation electrification is a promising solution to meet the ever-rising energy demand
and realize sustainable development. Lithium-ion batteries, being the most predominant …

Abstract Machine learning (ML) has shown to be an effective alternative to physical models
for quality prediction and process optimization of metal additive manufacturing (AM) …

Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …

Despite its great success, machine learning can have its limits when dealing with insufficient
training data. A potential solution is the additional integration of prior knowledge into the …

This review discusses Operator Inference, a nonintrusive reduced modeling approach that
incorporates physical governing equations by defining a structured polynomial form for the …

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 …

Abstract We present Lift & Learn, a physics-informed method for learning low-dimensional
models for large-scale dynamical systems. The method exploits knowledge of a system's …

A common strategy for the dimensionality reduction of nonlinear partial differential equations
(PDEs) relies on the use of the proper orthogonal decomposition (POD) to identify a reduced …

To overcome the limitations associated with purely physics-based and data-driven modeling
methods, hybrid, physics-based data-driven models have been developed, with improved …

Data Assimilation (DA) is the approximation of the true state of some physical system by
combining observations with a dynamic model. DA incorporates observational data into a …