Frontiers

Advancing Computational Material Science and Mechanics through Integrated Deep Learning Models

The field of computational materials science has been profoundly transformed by integrating deep learning and other machine learning methodologies. These sophisticated data-driven approaches have ...
VoxelMatters

Physics-based machine learning unlocks 3D printing potential

Physics-based machine learning unlocks 3D printing potential, thanks to work from Lehigh University's Parisa Khodabakhshi.

Researcher improves century-old equation to predict movement of dangerous air pollutants

A new method developed at the University of Warwick offers the first simple and predictive way to calculate how irregularly ...
IEEE

Versatile Bipedal Locomotion and Walking-Running Transition: Coordinating Supervised Learning and Nonlinear Optimization

Abstract: Online gait planning plays a crucial role for the locomotion of humanoid robots. While simplified models often fail to capture critical dynamic features of the robot’s motion, making online ...
IEEE

DE-KAN: A Differential Evolution-Based Optimization Framework for Enhancing Kolmogorov-Arnold Networks in Complex Nonlinear Modeling

Abstract: Kolmogorov-Arnold Networks (KANs) offer superior fitting ability and interpretability compared to traditional Multi-Layer Perceptrons (MLPs). However, their complex structure and lengthy ...
AZoOptics

Low-Dimensional Materials in Nonlinear Optics

Advancements in nonlinear optics using 2D materials are transforming photonic devices, offering enhanced performance and ...