Results 81 to 90 of about 6,454,315 (251)

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

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +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

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

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

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

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

Evidential Physics-Informed Neural Networks [PDF]

We present a novel class of Physics-Informed Neural Networks that is formulated based on the principles of Evidential Deep Learning, where the model incorporates uncertainty quantification by learning parameters of a higher-order distribution.
McBeth, Rafe, Wang, Kuancheng, Tan, Hai Siong   +2 more
core  

Advanced Graph–Physics Hybrid Framework (AGPHF) for Holistic Integration of AI-Driven Graph- and Physics- Methodologies to Promote Resilient Wastewater Management in Dynamic Real-World Conditions

Applied Sciences
Wastewater treatment is evolving rapidly with the advent of advanced deep-learning AI, graph-based, and physics-informed approaches. This study integrates graph neural networks, physics-informed neural networks, and multi-agent reinforcement learning ...
Vasileios Alevizos, Nikitas Gerolimos, Zongliang Yue, Sabrina Edralin, Clark Xu, George A. Papakostas, Eleni Vrochidou, George Marnellos, Mousa Mustafa   +8 more
doaj   +1 more source

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