Image de couverture de livre

On Geometry Regularization in Autoencoder Reduced-Order Models with Latent Neural ODE Dynamics

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Osipov, Mikhail
Format:	Preprint
Publié:	2026
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

Sparse Random-Feature Neural Networks with Krylov-Based SVD for Singularly Perturbed ODE
par: Vaidyan, Kevin Kurian Thomas, et autres
Publié: (2026)

Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization
par: Xie, Xiaoyu, et autres
Publié: (2024)

GALDS: A Graph-Autoencoder-based Latent Dynamics Surrogate model to predict neurite material transport
par: Hsieh, Tsung Yeh, et autres
Publié: (2025)

Stability Preserving Data-driven Models With Latent Dynamics
par: Luo, Yushuang, et autres
Publié: (2022)

Toward Adaptive Non-Intrusive Reduced-Order Models: Design and Challenges
par: Hedayat, Amirpasha, et autres
Publié: (2026)

Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models
par: Huang, Daniel Zhengyu, et autres
Publié: (2025)

Discrete Solution Operator Learning for Geometry-Dependent PDEs
par: Bai, Jinshuai, et autres
Publié: (2026)

KROM: Kernelized Reduced Order Modeling
par: Bacho, Aras, et autres
Publié: (2026)

Generative Adversarial Reduced Order Modelling
par: Coscia, Dario, et autres
Publié: (2023)

Latent Space Inference via Paired Autoencoders
par: Hart, Emma, et autres
Publié: (2026)

PDEfuncta: Spectrally-Aware Neural Representation for PDE Solution Modeling
par: Jo, Minju, et autres
Publié: (2025)

MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Data
par: Lyu, Liyao, et autres
Publié: (2026)

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

A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling
par: Bonneville, Christophe, et autres
Publié: (2024)

jNO: A JAX Library for Neural Operator and Foundation Model Training
par: Armbruster, Leon, et autres
Publié: (2026)

Towards a Machine-Learned Poisson Solver for Low-Temperature Plasma Simulations in Complex Geometries
par: Siffa, Ihda Chaerony, et autres
Publié: (2023)

High-fidelity Multiphysics Modelling for Rapid Predictions Using Physics-informed Parallel Neural Operator
par: Yuan, Biao, et autres
Publié: (2025)

One-shot acceleration of transient PDE solvers via online-learned preconditioners
par: Khodak, Mikhail, et autres
Publié: (2025)

GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencoder
par: Bonneville, Christophe, et autres
Publié: (2023)

Hamiltonian Matching for Symplectic Neural Integrators
par: Canizares, Priscilla, et autres
Publié: (2024)

Spectral methods for Neural Integral Equations
par: Zappala, Emanuele
Publié: (2023)

Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition
par: Mou, Changhong, et autres
Publié: (2026)

Parameterized Physics-informed Neural Networks for Parameterized PDEs
par: Cho, Woojin, et autres
Publié: (2024)

Structure-Preserving Digital Twins via Conditional Neural Whitney Forms
par: Kinch, Brooks, et autres
Publié: (2025)

Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
par: Jekic, Aleksandra, et autres
Publié: (2025)

Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets
par: Propp, Adrienne M., et autres
Publié: (2025)

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

BEACONS: Bounded-Error, Algebraically-Composable Neural Solvers for Partial Differential Equations
par: Gorard, Jonathan, et autres
Publié: (2026)

DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs
par: Jiang, Zhaoxi, et autres
Publié: (2025)

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

Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations
par: Rao, Xiang, et autres
Publié: (2025)

Learning the Exact Time Integration Algorithm for Initial Value Problems by Randomized Neural Networks
par: Dong, Suchuan, et autres
Publié: (2025)

Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
par: Zheng, Haoyang, et autres
Publié: (2025)

Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEs
par: Škardová, Kateřina, et autres
Publié: (2024)

Hybrid Neural World Models
par: Lakshmanan, Pranav, et autres
Publié: (2026)

Error Analysis and Numerical Algorithm for PDE Approximation with Hidden-Layer Concatenated Physics Informed Neural Networks
par: Qian, Yianxia, et autres
Publié: (2024)

TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
par: Chen, Zhuo, et autres
Publié: (2024)

Latent Autoencoder Ensemble Kalman Filter for Nonlinear Data assimilation
par: Tong, Xin T., et autres
Publié: (2026)

Lattice-based Deep Neural Networks: Regularity and Tailored Regularization
par: Keller, Alexander, et autres
Publié: (2026)

Parallel-in-Time Probabilistic Numerical ODE Solvers
par: Bosch, Nathanael, et autres
Publié: (2023)