Neuromorphic computers, inspired by the architecture of the human brain, are proving surprisingly adept at solving complex ...

Coherence-modulated gravity: validated τ_coh = 1.4±0.2×10⁻¹² N·m signal via non-minimal coupling ξRΦ². Continuum limit Δτ ≈ 2.6×10⁻¹² N·m. Tabletop feasible with cryogenic torsion balance (~1 hr ...

New research details an intriguing new way to solve "unsolvable" algebra problems that go beyond the fourth degree – something that has generally been deemed impossible using traditional methods for ...

Polynomial equations are a cornerstone of modern science, providing a mathematical basis for celestial mechanics, computer graphics, market growth predictions and much more. But although most high ...

Partial differential equations (PDEs) are workhorses of science and engineering. They describe a vast range of phenomena, from flow around a ship’s hull, to acoustics in a concert hall, to heat ...

Masaki Kashiwara has won the 2025 Abel prize, sometimes called the Nobel prize of mathematics, for his work on algebraic analysis. Kashiwara, a professor at Kyoto University, Japan, received the award ...

Generative AI too unreliable to launch? Predictive AI will realize genAI's bold, often overzealous promise of autonomy – or at least a great deal of it. Predictive AI has the potential to do what ...

Abstract: In scenarios with limited available data, training the function-to-function neural PDE solver in an unsupervised manner is essential. However, the efficiency and accuracy of existing methods ...

DIMON, a new AI framework, accelerates modeling by solving partial differential equations efficiently, reducing computation times from days to seconds. Tested in heart simulations, it promises ...

Operator learning is a transformative approach in scientific computing. It focuses on developing models that map functions to other functions, an essential aspect of solving partial differential ...