Abstract: Fourier neural operator (FNO) is a recently proposed data-driven scheme to approximate the implicit operators characterized by partial differential equations (PDEs) between functional spaces ...

Tessellations aren’t just eye-catching patterns—they can be used to crack complex mathematical problems. By repeatedly reflecting shapes to tile a surface, researchers uncovered a method that links ...

Discover how Fourier Analysis breaks down complex time series data into simpler components to identify trends and patterns, despite its limitations in stock forecasting.

A talk, The Unreasonable Effectiveness of the Fourier Transform, was presented by [Joshua Wise] at Teardown 2025 in June last ...

Generative AI is becoming ubiquitous in everyday life. Large language models like ChatGPT can help answer questions, write ...

The positioning, navigation, and timing (PNT) information is fundamental to modern information systems. Over the years, people have invented many navigation systems to get PNT information, whereas ...

New research reveals why even state-of-the-art large language models stumble on seemingly easy tasks—and what it takes to fix it ...

Abstract: Aiming at the problems that the existing physical neural network model is not precise enough in calculating nonlinear problems in the field of engineering, and the pure data-driven method ...