Imagen de Portada

Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Guo, Alex, Graham, Michael D.
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Data-driven stochastic reduced-order modeling of parametrized dynamical systems
por: Ilersich, Andrew F., et al.
Publicado: (2026)

Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning
por: Alam, Mohammad Shah, et al.
Publicado: (2025)

On latent dynamics learning in nonlinear reduced order modeling
por: Farenga, Nicola, et al.
Publicado: (2024)

Stochastic generative methods for stable and accurate closure modeling of chaotic dynamical systems
por: Williams, Emily, et al.
Publicado: (2025)

ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution
por: Zhou, Zhengyang, et al.
Publicado: (2024)

MATEY: multiscale adaptive foundation models for spatiotemporal physical systems
por: Zhang, Pei, et al.
Publicado: (2024)

Adjoint-based optimization with quantized local reduced-order models for spatiotemporally chaotic systems
por: Ozan, Defne E., et al.
Publicado: (2026)

Predicting two-dimensional spatiotemporal chaotic patterns with optimized high-dimensional hybrid reservoir computing
por: Nakano, Tamon, et al.
Publicado: (2025)

An adjoint method for training data-driven reduced-order models
por: Liu, Donglin, et al.
Publicado: (2026)

Coarse graining and reduced order models for plume ejection dynamics
por: Salas, Ike Griss, et al.
Publicado: (2025)

Generative emulation of chaotic dynamics with coherent prior
por: Nathaniel, Juan, et al.
Publicado: (2025)

Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system
por: Teutsch, Philipp, et al.
Publicado: (2025)

A discrete physics-informed training for projection-based reduced order models with neural networks
por: Sibuet, N., et al.
Publicado: (2025)

Machine-learning invariant foliations in forced systems for reduced order modelling
por: Szalai, Robert
Publicado: (2024)

Panda: A pretrained forecast model for chaotic dynamics
por: Lai, Jeffrey, et al.
Publicado: (2025)

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics
por: Wang, Zijian, et al.
Publicado: (2025)

Data-driven local operator finding for reduced-order modelling of plasma systems: II. Application to parametric dynamics
por: Faraji, Farbod, et al.
Publicado: (2024)

Improved deep learning of chaotic dynamical systems with multistep penalty losses
por: Chakraborty, Dibyajyoti, et al.
Publicado: (2024)

Non-linear reduced modeling of dynamical systems using kernel methods and low-rank approximation
por: Héas, Patrick, et al.
Publicado: (2017)

Machine-learning optimized measurements of chaotic dynamical systems via the information bottleneck
por: Murphy, Kieran A., et al.
Publicado: (2023)

Building symmetries into data-driven manifold dynamics models for complex flows: application to two-dimensional Kolmogorov flow
por: De Jesús, Carlos E. Pérez, et al.
Publicado: (2023)

Inferring bifurcation diagrams of two distinct chaotic systems by a single machine
por: Guo, Jianmin, et al.
Publicado: (2026)

Stability analysis of chaotic systems in latent spaces
por: Özalp, Elise, et al.
Publicado: (2024)

Coupling the reduced-order model and the generative model for an importance sampling estimator
por: Wan, Xiaoliang, et al.
Publicado: (2019)

Parallel BiLSTM-Transformer networks for forecasting chaotic dynamics
por: Ma, Junwen, et al.
Publicado: (2025)

Stochastic parameter reduced-order model based on hybrid machine learning approaches
por: Fang, Cheng, et al.
Publicado: (2024)

Generative diffusion models for spatiotemporal influenza forecasting
por: Lemaitre, Joseph, et al.
Publicado: (2026)

Zero-shot forecasting of chaotic systems
por: Zhang, Yuanzhao, et al.
Publicado: (2024)

Physics-informed reduced order model with conditional neural fields
por: Kim, Minji, et al.
Publicado: (2024)

Physically consistent and uncertainty-aware learning of spatiotemporal dynamics
por: Xu, Qingsong, et al.
Publicado: (2025)

Stabilizing autoregressive forecasts in chaotic systems via multi-rate latent recurrence
por: Dhingra, Mrigank, et al.
Publicado: (2026)

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

Decision-aware training of spatiotemporal forecasting models to select a top K subset of sites for intervention
por: Heuton, Kyle, et al.
Publicado: (2025)

Cyclic image generation using chaotic dynamics
por: Tanaka, Takaya, et al.
Publicado: (2024)

PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs
por: Brivio, Simone, et al.
Publicado: (2024)

Inferring stability properties of chaotic systems on autoencoders' latent spaces
por: Özalp, Elise, et al.
Publicado: (2024)

Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling
por: Galioto, Nicholas, et al.
Publicado: (2024)

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
por: Liu, Xin-Yang, et al.
Publicado: (2022)

Fourier neural operators for spatiotemporal dynamics in two-dimensional turbulence
por: Atif, Mohammad, et al.
Publicado: (2024)

Data-driven local operator finding for reduced-order modelling of plasma systems: I. Concept and verifications
por: Faraji, Farbod, et al.
Publicado: (2024)