Image de couverture de livre

Structure-Preserving Digital Twins via Conditional Neural Whitney Forms

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Kinch, Brooks, Shaffer, Benjamin, Armstrong, Elizabeth, Meehan, Michael, Hewson, John, Trask, Nathaniel
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Numerical Analysis Computational Physics
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Physics-informed sensor coverage through structure preserving machine learning
par: Shaffer, Benjamin David, et autres
Publié: (2025)

A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting
par: Kinch, Brooks, et autres
Publié: (2026)

Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs
par: Shaffer, Benjamin D., et autres
Publié: (2026)

Multi-robot Multi-source Localization in Complex Flows with Physics-Preserving Environment Models
par: Shaffer, Benjamin, et autres
Publié: (2025)

A meshfree exterior calculus for generalizable and data-efficient learning of physics from point clouds
par: Shaffer, Benjamin D., et autres
Publié: (2026)

A Structure-Preserving Domain Decomposition Method for Data-Driven Modeling
par: Jiang, Shuai, et autres
Publié: (2024)

Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning
par: Bistrian, Diana Alina, et autres
Publié: (2022)

Frequency-domain alignment of heterogeneous, multidimensional separations data through complex orthogonal Procrustes analysis
par: Armstrong, Michael Sorochan
Publié: (2025)

Mathematics of Digital Twins and Transfer Learning for PDE Models
par: Zong, Yifei, et autres
Publié: (2025)

Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training
par: Chang, Che-Chia, et autres
Publié: (2026)

Mathematical Opportunities in Digital Twins (MATH-DT)
par: Antil, Harbir
Publié: (2024)

Targeted Digital Twin via Flow Map Learning and Its Application to Fluid Dynamics
par: Chen, Qifan, et autres
Publié: (2025)

WINO: A Weak-Form Physics Informed Neural Operator for Hyperelasticity on Variable Domains
par: Zhu, Bokai, et autres
Publié: (2026)

A Structure-Preserving Graph Neural Solver for Parametric Hyperbolic Conservation Laws
par: Jiang, Jiamin, et autres
Publié: (2026)

THINNs: Thermodynamically Informed Neural Networks
par: Castro, Javier, et autres
Publié: (2025)

Monotone Peridynamic Neural Operator for Nonlinear Material Modeling with Conditionally Unique Solutions
par: Wang, Jihong, et autres
Publié: (2025)

Energy-Preserving Reduced Operator Inference for Efficient Design and Control
par: Koike, Tomoki, et autres
Publié: (2024)

Filtered Neural Galerkin model reduction schemes for efficient propagation of initial condition uncertainties in digital twins
par: Ning, Zhiyang, et autres
Publié: (2025)

SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
par: Gao, Yihang, et autres
Publié: (2022)

Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution Equations
par: Bruna, Joan, et autres
Publié: (2022)

Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks
par: Rusch, T. Konstantin, et autres
Publié: (2024)

Adaptive Error-Bounded Hierarchical Matrices for Efficient Neural Network Compression
par: Mango, John, et autres
Publié: (2024)

The Proof of Kolmogorov-Arnold May Illuminate Neural Network Learning
par: Freedman, Michael H.
Publié: (2024)

Gaussian Variational Schemes on Bounded and Unbounded Domains
par: Actor, Jonas A., et autres
Publié: (2024)

A Neural-preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions
par: Lan, Kai Weixian, et autres
Publié: (2023)

Geometry-Preserving Encoder/Decoder in Latent Generative Models
par: Lee, Wonjun, et autres
Publié: (2025)

Neural Low-Discrepancy Sequences
par: Van Huffel, Michael Etienne, et autres
Publié: (2025)

Operator SVD with Neural Networks via Nested Low-Rank Approximation
par: Ryu, J. Jon, et autres
Publié: (2024)

Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
par: Lowery, Matthew, et autres
Publié: (2024)

Parametric model reduction of mean-field and stochastic systems via higher-order action matching
par: Berman, Jules, et autres
Publié: (2024)

Weak Form Scientific Machine Learning: Test Function Construction for System Identification
par: Tran, April, et autres
Publié: (2025)

Energy Dissipation Preserving Physics Informed Neural Network for Allen-Cahn Equations
par: Kütük, Mustafa, et autres
Publié: (2024)

Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks
par: Zhang, Borong, et autres
Publié: (2022)

Unsupervised Surrogate-Assisted Synthesis of Free-Form Planar Antenna Topologies for IoT Applications
par: Askaripour, Khadijeh, et autres
Publié: (2026)

SpectraKAN: Conditioning Spectral Operators
par: Cheng, Chun-Wun, et autres
Publié: (2026)

Neural non-canonical Hamiltonian dynamics for long-time simulations
par: Courtès, Clémentine, et autres
Publié: (2025)

Smoothness Adaptivity in Constant-Depth Neural Networks: Optimal Rates via Smooth Activations
par: Liu, Yuhao, et autres
Publié: (2026)

Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks
par: Baptista, Ricardo, et autres
Publié: (2025)

Data-Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method
par: Alegría, Samuel A. Cruz, et autres
Publié: (2025)

Pruning AMR: Efficient Visualization of Implicit Neural Representations via Weight Matrix Analysis
par: Zvonek, Jennifer, et autres
Publié: (2025)