Results 121 to 130 of about 11,198,121 (288)
Physics-informed machine learning for moving load problems
, 2023 This paper presents a new approach to simulate forward and inverse problems of moving loads using physics-informed machine learning (PIML). Physics-informed neural networks (PINNs) utilize the underlying physics of moving load problems and aim to predict
Núñez, Alfredo +3 more
core
Development in metal multiaxial fatigue life prediction based on physics-informed neural network
Jixie qiangduThe research on multiaxial fatigue life prediction of materials is one of the critical elements in ensuring the structural integrity of components. In recent years, machine learning, especially neural networks, has been widely applied in fatigue life ...
ZHANG Zhuanli, SUN Xingyue, CHEN Xu
doaj
Advanced Functional Materials, EarlyView.Mask‐encoded UV exposure translates digital color information into pixel‐resolved structural‐color patterns in hydroxypropyl cellulose gels. Gaussian‐process‐guided calibration links UV processing conditions, wavelength, and mask opacity, enabling parallel fabrication of multicolor images in a single 10 s exposure.
Mengting Ye +8 more
wiley +1 more source
PHYSICS-INFORMED MACHINE LEARNING FOR PREDICTING THE BALLISTIC LIMIT OF WHIPPLE SHIELDS
PHYSICS-INFORMED MACHINE LEARNING FOR PREDICTING THE BALLISTIC LIMIT OF WHIPPLE ...
Shannon Ryan (13092264) +4 more
core
Point neuron learning: a new physics-informed neural network architecture
EURASIP Journal on Audio, Speech, and Music ProcessingMachine learning and neural networks have advanced numerous research domains, but challenges such as large training data requirements and inconsistent model performance hinder their application in certain scientific problems. To overcome these challenges,
Hanwen Bi, Thushara D. Abhayapala
doaj +1 more source
Physics-Aware Machine Learning Approach for High-Precision Quadcopter Dynamics Modeling
DronesIn this paper, we propose a physics-informed neural network controller for quadcopter dynamics modeling. Physics-aware machine learning methods, such as physics-informed neural networks, consider the UAV dynamics model, solving the system of ordinary ...
Ruslan Abdulkadirov +4 more
doaj +1 more source
Advanced Functional Materials, EarlyView.A degradable, skin‐conformal bioelectronic patch combines a starch‐dominated nanocomposite conductor with a gelatin substrate to enable sensitive strain sensing, high‐fidelity electrocardiography, electromyography, electrooculography, and machine‐learning‐assisted gesture control.
Ming Dong +10 more
wiley +1 more source
Advanced Functional Materials, EarlyView.Neuromorphic plasticity in OECTs is governed by the post‐stimulus evolution of coupled ionic and electronic states. Semiconductor chemistry and morphology, electrolyte composition and interfaces, and device architecture regulate ion injection, transport, confinement, and release, shifting responses from rapidly relaxing STP to metastable memory and ...
Inho Lee +9 more
wiley +1 more source
On the failure of ReLU activation for physics-informed machine learning
CoRRPhysics-informed machine learning uses governing ordinary and/or partial differential equations to train neural networks to represent the solution field. Like any machine learning problem, the choice of activation function influences the characteristics and performance of the solution obtained from physics-informed training.
openaire +3 more sources
Advanced Functional Materials, EarlyView.A novel nonlinear infrasound sensor concept based on a free‐standing ion conductive gel membrane is introduced. Acoustic signals are directly transduced into an electrical signal using an internal application mechanisms (gating). The nonlinearity might be used for neuromorphic in‐sensor computing. ABSTRACT Measuring low‐frequency or infrasound acoustic
Maarten Mittmann +6 more
wiley +1 more source