Results 131 to 140 of about 18,341 (302)

Dual-Balancing for Physics-Informed Neural Networks

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Physics-informed neural networks (PINNs) have emerged as a new learning paradigm for solving partial differential equations (PDEs) by enforcing the constraints of physical equations, boundary conditions (BCs), and initial conditions (ICs) into the loss function. Despite their successes, vanilla PINNs still suffer from poor accuracy and slow convergence
Chenhong Zhou, Jie Chen 0026, Zaifeng Yang, Ching Eng Png   +3 more
openaire   +2 more sources

Oxidized MoS2‐Based Multifunctional Memristive Hardware for Energy‐Efficient mmWave Signal Processing and In‐Memory Matrix Multiplication

Advanced Functional Materials, EarlyView.
Thermally oxidized MoS2‐based radio‐frequency switches enable a multifunctional platform that unifies broadband RF switching and in‐memory computation. The device achieves a cutoff frequency of 33.2 THz with high energy efficiency and supports hardware‐aware signal processing.
Juho Son, Changwoo Pyo, Sungmoon Park, Yoongi Cho, Seungchan Lee, Myungsoo Kim   +5 more
wiley   +1 more source

M‐ENIAC: A Physics‐Informed Machine Learning Recreation of the First Successful Numerical Weather Forecasts

Geophysical Research Letters
In 1950 the first successful numerical weather forecast was obtained by solving the barotropic vorticity equation using the Electronic Numerical Integrator and Computer (ENIAC), which marked the beginning of the age of numerical weather prediction. Here,
Rüdiger Brecht, Alex Bihlo
doaj   +1 more source

Physics-Informed Graph Neural Networks for Attack Path Prediction

Journal of Cybersecurity and Privacy
The automated identification and evaluation of potential attack paths within infrastructures is a critical aspect of cybersecurity risk assessment. However, existing methods become impractical when applied to complex infrastructures.
Marin François, Pierre-Emmanuel Arduin, Myriam Merad   +2 more
doaj   +1 more source

Loss-attentional physics-informed neural networks

Journal of Computational Physics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Song, Y., Wang, H., Yang, H., Taccari, M.L., Chen, X.   +4 more
openaire   +3 more sources

Multifunctional Gold Nanocluster‐Based PROTAC System for Targeted Degradation of Phosphorylated Tau and Modulation of Neuroinflammation in Alzheimer's Disease

Advanced Functional Materials, EarlyView.
We present a novel proteolysis‐targeting chimera (PROTAC) system conjugated to lipoic acid gold nanoclusters (PLANC), designed to degrade pTau, regulate inflammatory signaling, and effectively traverse the blood‐brain barrier (BBB). PLANC degraded pTau at various phosphorylation sites, with mechanistic studies confirming proteasome‐mediated degradation
Sarah Nevins, Callan D. McLoughlin, Wan‐Kyu Ko, Hye Kyu Choi, Brandon Conklin, Joshua B. Stein, Yannan Hou, Hongwon Kim, Rohan Khulbe, Ki‐Bum Lee   +9 more
wiley   +1 more source

Sound source directivity interpolation with physics-informed neural networks [PDF]

LAUREA MAGISTRALENel campo della realtà aumentata/virtuale, la riproduzione della direttività delle sorgenti sonore è fondamentale per un’esperienza immersiva di qualità.
Morena, Edoardo
core  

Learning strategies for physics-informed neural networks

Physics-informed neural networks (PINNs) are at the forefront of scientific machine learning, making possible the creation of machine intelligence that is cognizant of physical laws and able to accurately simulate them. This thesis studies the challenges
Wong, Jian Cheng
core   +1 more source

Transparent and Robust LiCl–Organohydrogel Triboelectric Nanogenerator With Deep Learning Assisted Sensing

Advanced Functional Materials, EarlyView.
Develop a LiCl–PEI–PAM hydrogel with 3000% stretchability and excellent optical transparency. Through comparative studies of various salts, confirm that LiCl is the most suitable salt for high TENG output. Achieve excellent freeze‐resistant, dry‐resistant, and rapid self‐healing (10 s) properties even in extreme environments. Balance ionic conductivity,
Hai Anh Thi Le, Thien Trung Luu, Duc‐Manh Dinh, Kyehan Rhee, Kyungwho Choi, Dukhyun Choi, Yong Tae Park   +6 more
wiley   +1 more source

Inferring Cosmological Parameters with Evidential Physics-Informed Neural Networks

Universe
We examine the use of a novel variant of Physics-Informed Neural Networks to predict cosmological parameters from recent supernovae and baryon acoustic oscillations (BAO) datasets. Our machine learning framework generates uncertainty estimates for target
Hai Siong Tan
doaj   +1 more source