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 ...

The Department of Energy's Oak Ridge National Laboratory has launched a novel robotic platform to rapidly analyze plant root systems as they grow, yielding AI-ready data to accelerate the development ...

In 1992 the QE2 ran aground on a shoal that should have been safely below her keel. This analysis explains under keel clearance, outdated chart data, and the physics of squat that made deep water ...

Abstract: Accurate Remaining Useful Lifetime (RUL) prediction for Insulated-Gate Bipolar Transistors (IGBTs) is critical for power device (PD) reliability and health management, yet challenged by the ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

With the exponential growth in demand for high-speed data transmission, the 5G system infrastructure, despite its impressive peak data rate of 10 gigabits per second, is increasingly inadequate for ...

Abstract: We propose the Physics-Informed Neural Network-driven Sparse Field Discretization method (PINN-SFD), a novel self-supervised, physics-informed deep learning approach for addressing the ...

One of the key steps in developing new materials is property identification, which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A ...

The knowledge-informed deep learning (KIDL) paradigm, with the blue section representing the LLM workflow (teacher demonstration), the orange section representing the distillation pipeline of KIDL ...

Aug. 21, 2025 — Lawrence Livermore National Laboratory (LLNL) researchers employed an AI-driven model to predict fusion ignition days ahead of the historic 2022 shot, according to a new study in ...