Constraint free physics-informed machine learning for micromagnetic energy minimization
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Publication:6543808
DOI10.1016/J.CPC.2024.109202MaRDI QIDQ6543808
Publication date: 25 May 2024
Published in: Computer Physics Communications (Search for Journal in Brave)
Cayley transformextreme learning machineR-functionsphysics-informed neural networkmicromagnetic energy minimizationstray field computation
Cites Work
- Title not available (Why is that?)
- Non-uniform FFT for the finite element computation of the micromagnetic scalar potential
- Fast stray field computation on tensor grids
- On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals
- On completeness of RFM solution structures
- Multilayer feedforward networks are universal approximators
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks
- PFNN: a penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries
- Prediction of magnetization dynamics in a reduced dimensional feature space setting utilizing a low-rank kernel method
- When and why PINNs fail to train: a neural tangent kernel perspective
- Learning time-stepping by nonlinear dimensionality reduction to predict magnetization dynamics
- On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks
- Conditional physics informed neural networks
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
- Numerical Optimization
- A Multivariate Faa di Bruno Formula with Applications
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
- Physics-Informed Neural Networks with Hard Constraints for Inverse Design
- Semi-analytic geometry with R-functions
- Transfinite interpolation over implicitly defined sets
- Preconditioned nonlinear conjugate gradient method for micromagnetic energy minimization
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