Imagen de Portada

Physics aware machine learning for micromagnetic energy minimization: recent algorithmic developments

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Schaffer, Sebastian, Schrefl, Thomas, Oezelt, Harald, Mauser, Norbert J, Exl, Lukas
Formato:	Preprint
Publicado:	2024
Materias:	Computational Physics Machine Learning 62P35, 68T07, 65Z05
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Higher order stray field computation on tensor product domains
por: Exl, Lukas, et al.
Publicado: (2025)

Physics-informed low-rank neural operators with application to parametric elliptic PDEs
por: Schaffer, Sebastian, et al.
Publicado: (2025)

Integrating Attendance Tracking and Emotion Detection for Enhanced Student Engagement in Smart Classrooms
por: Ainebyona, Keith, et al.
Publicado: (2026)

Data Completion for Electrical Impedance Tomography by Conditional Diffusion Models
por: Chen, Ke, et al.
Publicado: (2026)

Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
por: Li, Li'ang, et al.
Publicado: (2023)

Vanilla Feedforward Neural Networks as a Discretization of Dynamical Systems
por: Duan, Yifei, et al.
Publicado: (2022)

Achieving Universal Approximation and Universal Interpolation via Nonlinearity of Control Families
por: Cai, Yongqiang, et al.
Publicado: (2025)

Near-field-free super-potential FFT method for the three-dimensional free-space Poisson equation
por: Exl, Lukas, et al.
Publicado: (2025)

An energy-based deep splitting method for the nonlinear filtering problem
por: Bågmark, Kasper, et al.
Publicado: (2022)

A Minimal Control Family of Dynamical Systems for Universal Approximation
por: Duan, Yifei, et al.
Publicado: (2023)

Surrogate-based multilevel Monte Carlo methods for uncertainty quantification in the Grad-Shafranov free boundary problem
por: Elman, Howard, et al.
Publicado: (2025)

A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
por: Aich, Agnideep, et al.
Publicado: (2025)

Event-Level Probabilistic Prediction of Extreme Rainfall over India Using Physics-Gated Latent Dynamics
por: Neelan, Arun Govind
Publicado: (2026)

On the Benefits of Accelerated Optimization in Robust and Private Estimation
por: Marchis, Laurentiu Andrei, et al.
Publicado: (2025)

On the Value of Tokeniser Pretraining in Physics Foundation Models
por: Sotoudeh, Hadi, et al.
Publicado: (2026)

Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning
por: Kratsios, Anastasis, et al.
Publicado: (2026)

Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional scarce data
por: Gu, Hyemin, et al.
Publicado: (2022)

Physics-informed features in supervised machine learning
por: Lampani, Margherita, et al.
Publicado: (2025)

How Can Deep Neural Networks Fail Even With Global Optima?
por: Guan, Qingguang
Publicado: (2024)

Neural-HSS: Hierarchical Semi-Separable Neural PDE Solver
por: Sittoni, Pietro, et al.
Publicado: (2026)

Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI
por: Terhag, F., et al.
Publicado: (2023)

High-dimensional Bayesian filtering through deep density approximation
por: Bågmark, Kasper, et al.
Publicado: (2025)

Nonlinear filtering based on density approximation and deep BSDE prediction
por: Bågmark, Kasper, et al.
Publicado: (2025)

A Modular Deep Learning-based Approach for Diffuse Optical Tomography Reconstruction
por: Benfenati, Alessandro, et al.
Publicado: (2024)

A Systematic Evaluation of Imbalance Handling Methods in Biomedical Binary Classification
por: Chen, Jiandong, et al.
Publicado: (2026)

Operator Learning Using Random Features: A Tool for Scientific Computing
por: Nelsen, Nicholas H., et al.
Publicado: (2024)

Riemannian AmbientFlow: Towards Simultaneous Manifold Learning and Generative Modeling from Corrupted Data
por: Diepeveen, Willem, et al.
Publicado: (2026)

FieldTNN-based machine learning method for Maxwell eigenvalue problems
por: Jiang, Jiantao, et al.
Publicado: (2024)

A convergent scheme for the Bayesian filtering problem based on the Fokker--Planck equation and deep splitting
por: Bågmark, Kasper, et al.
Publicado: (2024)

Evaluating the Quality of the Quantified Uncertainty for (Re)Calibration of Data-Driven Regression Models
por: Wibbeke, Jelke, et al.
Publicado: (2025)

Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
por: Wang, Yueyang, et al.
Publicado: (2026)

Learning where to learn: Training data distribution optimization for scientific machine learning
por: Guerra, Nicolas, et al.
Publicado: (2025)

Bayesian--AI Fusion for Epidemiological Decision Making: Calibrated Risk, Honest Uncertainty, and Hyperparameter Intelligence
por: Chatterjee, Debashis
Publicado: (2025)

Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
por: Novák, Vojtěch, et al.
Publicado: (2026)

Robust Fourier Neural Networks
por: Jeong, Halyun, et al.
Publicado: (2024)

AI and Deep Learning for Automated Segmentation and Quantitative Measurement of Spinal Structures in MRI
por: Shastry, Praveen, et al.
Publicado: (2025)

Conditional correlation estimation and serial dependence identification
por: Pączek, Kewin, et al.
Publicado: (2024)

SAGA: A Sequence-Adaptive Generative Architecture for Multi-Horizon Probabilistic Forecasting with Adaptive Temporal Conformal Prediction
por: Lundström-Imanov, Gustav Olaf Yunus Laitinen-Fredriksson, et al.
Publicado: (2026)

Leveraging Imperfection with MEDLEY A Multi-Model Approach Harnessing Bias in Medical AI
por: Abtahi, Farhad, et al.
Publicado: (2025)

Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning
por: Zou, Zongren, et al.
Publicado: (2024)