Researchers from UC Berkeley are utilizing deep learning to bypass iterative finite element calculations. Their deep learning model is trained using finite element simulation data to predict the deformation behavior of favorable designs.
Computational electromagnetics: Know your tools for they shape our future
Clarifying some of the misconceptions, limitations, and capabilities of CEM modelling to help researchers find the right tools for their needs.
Nanoscale antennas for optical data communication
German physicists converted electrical signals into photons and radiated them in specific directions using a nanometer-scale antenna.
Can AI solve the mysteries of photonic nanostructures?
A new approach seeks to use the “intelligence” aspects of AI to understand the physics of photonic nanostructures.
New Insights Into Old Chemical Concepts Using DFT
Recent progress in density functional theory provide new insights for chemical concepts like electrophilicity, nucleophilicity, regioselectivity, stereoselectivity, and more.
A Leap for Quantum Computing: Silicon Quantum Bits Establish a Long-Distance Relationship
Princeton scientists demonstrate that two silicon quantum bits can communicate across relatively long distances in a turning point for the technology.
Artificial intelligence: Cool but scary?
AI is growing at a rapid pace and solving complex problems in the process—but what are the risks of such a sophisticated technology?
Improving Image Recognition to Accelerate Machine Learning
By combining two types of memory arrays, researchers can accelerate image recognition for more efficient machine learning.
Storing Energy in Hydrogen
A novel catalyst with activity 20x higher than platinum could be used to store and retrieve energy stored in chemical bonds.
Evaluating Steelhead Migration Barriers with Drone-Based SFM
Researchers demonstrate the use of aerial drone photography, hydraulic modeling, and simulated rock blasting, to evaluate velocity barriers that prevent steelhead migrations.