Results 81 to 90 of about 6,454,505 (250)

Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings

We present a novel approach to hard-constrain Neumann boundary conditions in physics-informed neural networks (PINNs) using Fourier feature embeddings. Neumann boundary conditions are used to described critical processes in various application, yet they ...
Medvedev, Vlad, Straub, Christopher, Brendel, Philipp, Roßkopf, Andreas   +3 more
core   +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

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

Fabrication Routes for Ionic Conducting Fiber Strain Sensors

Advanced Engineering Materials, EarlyView.
Ionic conducting fiber strain sensors (ICFSs) offer compliant, textile‐integrable sensing. Thus far, the commercialization of ICFSs has been constrained by fiber fabrication routes. This review provides a fabrication‐centric analysis of ICFSs correlating processing strategies with material properties and scalability.
Leo John Kershaw, Zhanhong Yang, Anne Ladegaard Skov, Magdalena Skowyra   +3 more
wiley   +1 more source

Application of Physics informed Neural Networks

U ovom radu su istražene fizikom informirane neuronske mreže (engl. Physics-informed Neural Networks, PINN). Objašnjene su tehnike strojnog učenja i neuronskih mreža, kao i metode fizikalnog modeliranja i rješavanja diferencijalnih jednadžbi.
Maretić, Mislav
core   +1 more source

CL-PINN: A Physics-Informed Neural Networks Framework for Equatorial Tsunami Wave Propagation in Geophysical Ocean

IEEE Access
Wave dynamics are governed by linear and nonlinear partial differential equations, where prior physical knowledge in the form of differential equations plays a crucial role in simulating dynamical models.
Arup Kumar Sahoo, Swarup Kumar Sahoo, S. Chakraverty   +2 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

ELM-FBPINN: efficient finite-basis physics-informed neural networks [PDF]

Physics Informed Neural Networks (PINNs) offer several advantages when compared to traditional numerical methods for solving PDEs, such as being a mesh-free approach and being easily extendable to solving inverse problems.
Pestana, Jennifer, Dolean, Victorita, Anderson, Samuel, Moseley, Ben   +3 more
core   +2 more sources

Modeling sessile drop interfaces using physics-informed neural networks [PDF]

APL Machine Learning
We investigate the application of physics-informed neural networks (PINNs) to simulate sessile drops interacting with surfaces exhibiting heterogeneous wettability and contact angle hysteresis.
Hogan Choi, Leyun Feng, Wonjae Choi, Kyoo-Chul Park   +3 more
doaj   +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