Geometric analysis, at its core, integrates methods from differential geometry and partial differential equations to study the properties of spaces endowed with a notion of distance. Metric spaces, ...

The newly developed Huber mean provides a more stable and reliable way to compute averages for data lying on curved geometric spaces, or Riemannian manifolds. By combining the strengths of ...

Researchers have developed an efficient new way to quickly analyze complex geometric models by borrowing a computational approach that has made photorealistic animated films possible. Researchers at ...

Debates over how geometry is understood and learned date back at least to the days of Plato, with more recent scholars concluding that only humans possess the foundations of this understanding.

Video: What about shape data? How does that factor into new AI/ML models? We've heard a lot – an amazing amount, actually – about data science applications for AI, with media like images, videos, ...

Saksman's research deals with several mathematical problem areas that involve probabilistic questions in various setups. These include probabilistic methods in mathematical physics, analysis and ...

Guillaume Aubrun and I wrote a book focused on the interface between mathematical aspects of Quantum Information Theory and local theory of Banach spaces, a field which studies the properties of (very ...

The QUASAR™ MRID3D Geometric Distortion Analysis System from Modus QA is a new way to quantify magnetic resonance image (MRI) distortion for the entire 3D volume… automatically! The turn-key solution ...

Geometric optics is a confusing subject for many physics students, who often first encounter the subject in introductory college physics classes. Traditional instruction in geometric optics is not as ...

Geometric analysis of metric spaces is a vibrant research area at the interface of analysis, geometry and topology. It examines the intrinsic structure of spaces endowed with a notion of distance, ...