Modeling, Computation, Simulation

This illustration shows how ZENN, a new kind of AI model, helps computers make sense of messy, real-world information. The flowing, multicolored surface represents the many possible patterns hiding inside complex data, even when that data comes in very different forms. Images, written text and location data — shown by the icons at the bottom — are all combined into one shared picture. By bringing these different sources together, ZENN can spot meaningful patterns and make better predictions than traditional models that struggle with inconsistent or imperfect data. Credit: Jennifer M. McCann.

Artificial intelligence (AI) is increasingly used to analyze medical images, materials data and scientific measurements, but many systems struggle when real-world data do not match ideal conditions. Measurements collected from different instruments, experiments or simulations often vary widely in resolution, noise and reliability. Traditional machine-learning models typically assume those differences are negligible — an assumption that can limit accuracy and trustworthiness.

Read more about From brain scans to alloys: Teaching AI to make sense of complex research data

A group of researchers at Penn State developed an approach that integrates artificial intelligence into metasurface design. Their work was featured on the cover of the October issue of Nanophotonics. Credit: Provided by Huanshu Zhang.

A team of researchers at Penn State have devised a new, streamlined approach to design metasurfaces, a class of engineered materials that can manipulate light and other forms of electromagnetic radiation with just their structures. This rapid optimization process could help manufacture advanced optical systems like camera lenses, virtual reality headsets, holographic imagers and more, the team said.

Read more about AI approach takes optical system design from months to milliseconds

From left to right, Albert Suceava, a doctoral student in materials science and engineering, Venkatraman Gopalan, a professor of materials science and engineering, and Saugata Sarker, a graduate student in materials science and engineering, inspect polar domains made visible using nonlinear optical microscopy. Credit: Seana Wood/Penn State.

Materials scientists can learn a lot about a sample material by shooting lasers at it. With nonlinear optical microscopy — a specialized imaging technique that looks for a change in the color of intense laser light — researchers can collect data on how the light interacts with the sample and, through time-consuming and sometimes expensive analyses, characterize the material’s structure and other properties. Now, researchers at Penn State have developed a computational framework that can interpret the nonlinear optical microscopy images to characterize the material in microscopic detail.

Read more about Shedding light on materials in the physical, biological sciences

image showing motor proteins moved along a microtubule using single-molecule fluorescence microscopy

By Mariah R. Lucas

UNIVERSITY PARK, Pa. — Neurons, which are responsible for producing the signals that ultimately trigger an action like talking or moving a muscle, are built and maintained by classes of motor proteins that transport molecular cargo along elongated tracks called microtubules. A Penn State-led team of researchers uncovered how two main groups of motor proteins compete to transport cargo in opposite directions between the cell body and the synapse in neurons.

Read more about Neuron movements caused by push, pull of motor proteins, study finds

By Sarah Small

Amrita Basak, assistant professor of mechanical engineering at Penn State, has received a two-year, $498,235 Defense Advanced Research Projects Agency (DARPA) Young Faculty Award for her work predicting and preventing thermal deformation in multi-laser additive manufacturing (AM).