Results 111 to 120 of about 8,084,029 (292)

Physics-informed Neural Networks for Biopharma Applications

, 2021
Physics-Informed Neural Networks (PINNs) are hybrid models that incorporate differential equations into the training of neural networks, with the aim of bringing the best of both worlds.
Cedergren, Linnéa
core   +2 more sources

A Physics‐Informed Neural Network Approach to the Gannon Storm

Geophysical Research Letters
Extreme geomagnetic storms, such as the May 2024 Gannon event, pose significant risks to technological infrastructure, requiring robust forecasting models.
M. Lacal, E. Camporeale, M. Piersanti, G. Consolini   +3 more
doaj   +1 more source

Physics-Informed Graph Neural Network for Flight Dynamics Modeling

Aerospace
Flight dynamics modeling is a fundamental cornerstone of aircraft design, simulation, and control. Traditional approaches rely on aerodynamic look-up tables for numerical integration, which suffer from high data-acquisition costs, poor extrapolation ...
Liang Ma, Zhanwu Li, Juntao Zhang, You Li, Shijie Deng   +4 more
doaj   +1 more source

A Numerical–Experimental Approach for Multi‐Matrix Fiber‐Reinforced Plastics Characterization Using Finite Element Model Updating

Advanced Engineering Materials, EarlyView.
A numerical–experimental framework is developed for characterizing multi‐matrix fiber‐reinforced polymers (MM‐FRPs) combining epoxy and polyurethane matrices. Harmonic bending tests are integrated with finite element model updating (FEMU) to simultaneously identify elastic and viscoelastic material parameters.
Rodrigo M. Dartora, Tales V. Lisbôa, Axel Spickenheuer, Rogério J. Marczak, Jakson M. Vassoler   +4 more
wiley   +1 more source

Neuromorphic, physics-informed spiking neural network for molecular dynamics

Machine Learning: Science and Technology
Molecular dynamics (MD) simulations are used across many fields from chemical science to engineering. In recent years, Scientific Machine Learning (Sci-ML) in MD attracted significant attention and has become a new direction of scientific research ...
Vuong Van Pham, Temoor Muther, Amirmasoud Kalantari Dahaghi   +2 more
doaj   +1 more source

Two-Scale Physics-Informed Neural Networks for Structural Dynamics Parameter Inversion: Numerical and Experimental Validation on T-Shaped Tower Health Monitoring

Buildings
We present a two-scale physics-informed neural network (TSPINN) algorithm to address structural parameter inversion problems involving small parameters. The algorithm’s core mechanism directly embeds small parameters into the neural network architecture.
Xinpeng Liu, Xuemei Zhang, Yongli Zhong, Zhitao Yan, Yu Hong   +4 more
doaj   +1 more source

Investigation of Resonance Characteristics of More Than 10 000 Metal‐Coated Silicon MEMS Cantilevers Using Machine Learning

Advanced Engineering Materials, EarlyView.
More than 10 000 metal‐coated silicon MEMS cantilevers are fabricated and characterized to build a large‐scale experimental dataset spanning multiple vibrational modes. Machine learning algorithms are benchmarked to predict resonance frequency and quality factor, with ensemble based models achieving accuracy approaching experimental uncertainty.
Sina Zare Pakzad, Saurabh Thorat, Andre Loch Gesing, Dominik Huber, Christopher Zellner, Patrick Egger, Georg Pfusterschmied, Ulrich Schmid, Michael Schneider   +8 more
wiley   +1 more source

Learning Wiener Process-Based Degradation Model Via Physics-Informed Neural Network for Predicting Lithium-Ion Battery Remaining Useful Life

This paper introduces the Wiener-Informed Neural Network (WINN), an innovative Physics-Informed Neural Network (PINN) framework designed for the precise prediction of the remaining useful life (RUL) of lithium-ion batteries (LIB).
Abdollahi, Farzaneh, Torabi, Farshad, Mousavi, Ella, Barazandeh, Dr. Farshad, Hajizadeh, Amin; id_orcid   +4 more
core   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Spatio-temporal Attention-based Hidden Physics-informed Neural Network for Remaining Useful Life Prediction [PDF]

Predicting the Remaining Useful Life (RUL) is essential in Prognostic Health Management (PHM) for industrial systems. Although deep learning approaches have achieved considerable success in predicting RUL, challenges such as low prediction accuracy and ...
Xia, Min, Jiang, Feilong, Hou, Xiaonan
core   +2 more sources