Cover Image

A parametric framework for kernel-based dynamic mode decomposition using deep learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kevopoulos, Konstantinos, Ye, Dongwei
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computational Engineering, Finance, and Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Gaussian process learning of nonlinear dynamics
by: Ye, Dongwei, et al.
Published: (2023)

A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: efficient mapping of concrete microstructures to its fracture properties
by: Koopas, Rasoul Najafi, et al.
Published: (2024)

Operator-based machine learning framework for generalizable prediction of unsteady treatment dynamics in stormwater infrastructure
by: Shatarah, Mohamed, et al.
Published: (2025)

PDE-constrained Gaussian process surrogate modeling with uncertain data locations
by: Ye, Dongwei, et al.
Published: (2023)

Physics-informed neural networks for parameter learning of wildfire spreading
by: Vogiatzoglou, Konstantinos, et al.
Published: (2024)

Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations
by: Rezaei, Shahed, et al.
Published: (2024)

Data-driven uncertainty-aware seakeeping prediction of the Delft 372 catamaran using ensemble Hankel dynamic mode decomposition
by: Palma, Giorgio, et al.
Published: (2025)

C(NN)FD -- a deep learning framework for turbomachinery CFD analysis
by: Bruni, Giuseppe, et al.
Published: (2023)

Interpretable long-term traffic modelling on national road networks using theory-informed deep learning
by: Li, Yue, et al.
Published: (2026)

Utilising physics-guided deep learning to overcome data scarcity
by: Bai, Jinshuai, et al.
Published: (2022)

Model-based deep reinforcement learning for accelerated learning from flow simulations
by: Weiner, Andre, et al.
Published: (2024)

Orientation-aware interaction-based deep material network in polycrystalline materials modeling
by: Wei, Ting-Ju, et al.
Published: (2025)

Learning solutions of parametric Navier-Stokes with physics-informed neural networks
by: Naderibeni, M., et al.
Published: (2024)

Physics-based deep kernel learning for parameter estimation in high dimensional PDEs
by: Yan, Weihao, et al.
Published: (2025)

A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content
by: Bahtiri, Betim, et al.
Published: (2023)

A nonlinear extension of parametric model embedding for dimensionality reduction in parametric shape design
by: Serani, Andrea, et al.
Published: (2026)

Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraints
by: Nair, Siddharth, et al.
Published: (2025)

Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference
by: McQuarrie, Shane A., et al.
Published: (2025)

A mechanism-driven reinforcement learning framework for shape optimization of airfoils
by: Wang, Jingfeng, et al.
Published: (2024)

Rational kernel-based interpolation for complex-valued frequency response functions
by: Bect, Julien, et al.
Published: (2023)

GeOT: A spatially explicit framework for evaluating spatio-temporal predictions
by: Wiedemann, Nina, et al.
Published: (2024)

Learning Satellite Pattern-of-Life Identification: A Diffusion-based Approach
by: Ye, Yongchao, et al.
Published: (2024)

Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space
by: Koopas, Rasoul Najafi, et al.
Published: (2024)

Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing
by: Xie, Jiarui, et al.
Published: (2024)

Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties
by: Liu, Ning, et al.
Published: (2024)

Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls
by: Chen, Jungang, et al.
Published: (2023)

Inverse design of photonic surfaces on Inconel via multi-fidelity machine learning ensemble framework and high throughput femtosecond laser processing
by: Grbcic, Luka, et al.
Published: (2024)

Detection of anomalies in cow activity using wavelet transform based features
by: Guien, Valentin, et al.
Published: (2025)

Local learning for stable backpropagation-free neural network training towards physical learning
by: Guo, Yaqi, et al.
Published: (2026)

Design Editing for Offline Model-based Optimization
by: Yuan, Ye, et al.
Published: (2024)

evoxels: A differentiable physics framework for voxel-based microstructure simulations
by: Daubner, Simon, et al.
Published: (2025)

UAMM: Price-oracle based Automated Market Maker
by: Im, Daniel Jiwoong, et al.
Published: (2023)

How to integrate cloud service, data analytic and machine learning technique to reduce cyber risks associated with the modern cloud based infrastructure
by: Bhatta, Upakar
Published: (2024)

Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design
by: Khan, Shahroz, et al.
Published: (2024)

A quantitative analysis of knowledge-learning preferences in large language models in molecular science
by: Liu, Pengfei, et al.
Published: (2024)

Zero-shot adaptation to order book dynamics
by: Asadulaev, Arip
Published: (2026)

Thermodynamically consistent machine learning model for excess Gibbs energy
by: Hoffmann, Marco, et al.
Published: (2025)

XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change
by: Wei, Jiawen, et al.
Published: (2025)

Multifidelity linear regression for scientific machine learning from scarce data
by: Qian, Elizabeth, et al.
Published: (2024)

Beyond development: Challenges in deploying machine learning models for structural engineering applications
by: Esteghamati, Mohsen Zaker, et al.
Published: (2024)