Transportation electrification has been fueled by recent advancements in the technology
and manufacturing of battery systems, but the industry yet is facing serious challenges that …

With the growing number of wind farms over the last few decades and the availability of
large datasets, research in wind-farm flow modeling—one of the key components in …

Modeling and manipulating elasto-plastic objects are essential capabilities for robots to
perform complex industrial and household interaction tasks (eg, stuffing dumplings, rolling …

Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …

This survey investigates wall modeling in large-eddy simulations (LES) using data-driven
machine-learning (ML) techniques. To this end, we implement three ML wall models in an …

Computational fluid dynamics using the Reynolds-averaged Navier–Stokes (RANS) remains
the most cost-effective approach to study wake flows and power losses in wind farms. The …

Trajectory inference is a hard problem when states measurements are noisy and if there is
no high-fidelity model available for estimation; this may arise into high-variance and biased …

Developing accurate dynamic models for various systems is crucial for optimization, control,
fault diagnosis, and prognosis. Recent advancements in information technologies and …

This paper focuses on the use of reinforcement learning (RL) as a machine-learning (ML)
modeling tool for near-wall turbulence. RL has demonstrated its effectiveness in solving high …

Background Many cancer survivors experience cancer-related cognitive impairment (CRCI),
often with significant negative consequences across various life domains. Emerging …