Physics-informed parallel neural networks with self-adaptive loss weighting for the identification of continuous structural systems
DOI10.1016/J.CMA.2024.117042MaRDI QIDQ6557810
Aleksandra Radlińska, Author name not available (Why is that?), Rui Zhang
Publication date: 18 June 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
identificationneural tangent kernelcontinuous structural systemphysics-informed parallel neural networksself-adaptive loss weighting
Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Probabilistic methods, particle methods, etc. for initial value and initial-boundary value problems involving PDEs (65M75)
Cites Work
- Title not available (Why is that?)
- \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition
- Self-adaptive physics-informed neural networks
- Parallel physics-informed neural networks via domain decomposition
- When and why PINNs fail to train: a neural tangent kernel perspective
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
- On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks
- An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- An eigensystem realization algorithm for modal parameter identification and model reduction
- A Limited Memory Algorithm for Bound Constrained Optimization
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
- Combining machine learning and domain decomposition methods for the solution of partial differential equations—A review
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