Neuromorphic computers, inspired by the architecture of the human brain, are proving surprisingly adept at solving complex mathematical problems that underpin scientific and engineering challenges.
A $1 million prize awaits anyone who can show where the math of fluid flow breaks down. With specially trained AI systems, ...
GPT-5.2 Pro delivers a Lean-verified proof of Erdős Problem 397, marking a shift from pattern-matching AI to autonomous ...
The PIML4PDE framework is designed to solve Partial Differential Equations (PDEs) using Physics-Informed Machine Learning (PIML). This framework is intended for educational purposes, demonstrating ...
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential ...
Abstract: In the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning (DL) enables solving partial ...
Abstract: Physics-informed neural networks (PINNs) have recently been utilized to tackle wave equation-based forward and inverse problems. However, they encounter challenges in accurately predicting ...
🧑🚀 Astronauts after splashdown 🏀 Photos: Aztecs beat Lobos 🌼 Best things to do this week 🛍️ La Mesa Macy’s closing 🎢 Legoland’s new coaster ...
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