Neuromorphic computers modeled after the human brain can now solve the complex equations behind physics simulations — something once thought possible only with energy-hungry supercomputers. The ...
Mathematicians finally understand the behavior of an important class of differential equations that describe everything from water pressure to oxygen levels in human tissues. The trajectory of a storm ...
Abstract: Machine learning is a rapidly advancing field with diverse applications across various domains. One prominent area of research is the utilization of deep learning techniques for solving ...
This repository implements the Physics-Driven Orthogonal Feature Method (PD-OFM), a framework for solving Partial Differential Equations (PDEs) using physics-informed deep learning with orthogonality ...
Abstract: Optimized math libraries tailored to specific hardware architectures play a critical role in maximizing performance for large-scale scientific simulations. The Portable, Extensible Toolkit ...
This repository contains the official JAX implementation for the paper "Physics-informed Reduced Order Modeling of Time-dependent PDEs via Differentiable Solvers", accepted at NeurIPS 2025. Training ...
Imagine trying to design a key for a lock that is constantly changing its shape. That is the exact challenge we face in ...
Microsoft has warned that information-stealing attacks are "rapidly expanding" beyond Windows to target Apple macOS environments by leveraging cross-platform languages like Python and abusing trusted ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results