University of Texas at Austin

4 Results

Type

Tag

Current Filters Clear all

Remove Tags filter×

Fin-ion tunable transistor for ultra-low power computing

News Article, June 7, 2023 • Work on this project revealed fundamental principles of electrochemical random access memory and established a viable path toward its integration with complementary metal-oxide semiconductor. Data-heavy workflows such as AI require in to increase system efficiency. Work on this memory computing, so this LDRD team focused on creating analog resistive nonvolatile...

NM - 218372 - Ulta Low Power Computing.png

Optimizing machine learning decisions with prediction uncertainty

News Article, May 9, 2023 • Digital background depicting innovative technologies in (AI) artificial systems, neural interfaces and internet machine learning technologies While ML classifiers are widespread, output is often not part of a follow-on decision-making process because of lack of uncertainty quantification. Through this project, the team developed decision analysis methods that combined uncertainty estimates...

Digital background image of brain connectors

Revealing the kinetics of atmospheric corrosion damage through in-situ x-ray computed tomography and machine vision

News Article, June 8, 2023 • In-situ XCT enables both the growth rate and morphology (yellow) of pits to be directly characterized in relationship to the evolving electrolyte (blue) and prior stages of pit morphology (black). Atmospheric corrosion is a critical materials degradation problem, yet the ability to predict its kinetics remains elusive. Conventional approaches provide...

Using nonlocal interface problem allows for 7x speedup in large-scale simulations

News Article, May 9, 2023 • Multimaterial problems exist in mission applications such as mechanics and subsurface transport. To capture effects arising from long-range forces at the microscale and mesoscale that aren’t accounted for by classical partial differential equations, the MAThematical foundations for Nonlocal Interface Problems (MATNIP) project team developed a mathematically rigorous interface theory employing...