Copertina

Interpretable Spatial-Temporal Fusion Transformers: Multi-Output Prediction for Parametric Dynamical Systems with Time-Varying Inputs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Sun, Shuwen, Feng, Lihong, Benner, Peter
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Numerical Analysis
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Data-Augmented Predictive Deep Neural Network: Enhancing the extrapolation capabilities of non-intrusive surrogate models
di: Sun, Shuwen, et al.
Pubblicazione: (2024)

Subspace-Distance-Enabled Active Learning for Efficient Data-Driven Model Reduction of Parametric Dynamical Systems
di: Kapadia, Harshit, et al.
Pubblicazione: (2025)

Structure-preserving learning for multi-symplectic PDEs
di: Yıldız, Süleyman, et al.
Pubblicazione: (2024)

Reduced-Basis Deep Operator Learning for Parametric PDEs with Independently Varying Boundary and Source Data
di: Wang, Yueqi, et al.
Pubblicazione: (2025)

Using Parametric PINNs for Predicting Internal and External Turbulent Flows
di: Ghosh, Shinjan, et al.
Pubblicazione: (2024)

Guaranteed Stable Quadratic Models and their applications in SINDy and Operator Inference
di: Goyal, Pawan, et al.
Pubblicazione: (2023)

Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
di: Gosea, Ion Victor, et al.
Pubblicazione: (2024)

Efficient Parametric SVD of Koopman Operator for Stochastic Dynamical Systems
di: Jeong, Minchan, et al.
Pubblicazione: (2025)

Low-rank computation of the posterior mean in Multi-Output Gaussian Processes
di: Esche, Sebastian, et al.
Pubblicazione: (2025)

Deep Eigenspace Network for Parametric Non-self-adjoint Eigenvalue Problems
di: Li, H., et al.
Pubblicazione: (2025)

BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics
di: Liu, Yuxuan, et al.
Pubblicazione: (2025)

The Parametric Complexity of Operator Learning
di: Lanthaler, Samuel, et al.
Pubblicazione: (2023)

Interpolatory Necessary Optimality Conditions for Reduced-order Modeling of Parametric Linear Time-invariant Systems
di: Mlinarić, Petar, et al.
Pubblicazione: (2024)

Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems
di: He, Xiaolong, et al.
Pubblicazione: (2025)

Variational Matrix-Learning Fourier Networks for Parametric Multiphysics Surrogates
di: Li, Xinyu, et al.
Pubblicazione: (2026)

Multilevel CNNs for Parametric PDEs based on Adaptive Finite Elements
di: Schütte, Janina Enrica, et al.
Pubblicazione: (2024)

Discrete empirical interpolation in the tensor t-product framework
di: Chellappa, Sridhar, et al.
Pubblicazione: (2024)

Interpretable AI-Assisted Early Reliability Prediction for a Two-Parameter Parallel Root-Finding Scheme
di: Carpentieri, Bruno, et al.
Pubblicazione: (2026)

Hybrid Iterative Solvers with Geometry-Aware Neural Preconditioners for Parametric PDEs
di: Lee, Youngkyu, et al.
Pubblicazione: (2025)

AE-ViT: Stable Long-Horizon Parametric Partial Differential Equations Modeling
di: Mikuš, Iva, et al.
Pubblicazione: (2026)

Solving Inverse Parametrized Problems via Finite Elements and Extreme Learning Networks
di: Burman, Erik, et al.
Pubblicazione: (2026)

Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks
di: Bhandari, Diksha, et al.
Pubblicazione: (2023)

Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks
di: Wei, Zhi-Feng, et al.
Pubblicazione: (2025)

Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery
di: Liu, Ning, et al.
Pubblicazione: (2025)

Parametric model reduction of mean-field and stochastic systems via higher-order action matching
di: Berman, Jules, et al.
Pubblicazione: (2024)

Artifacts of Numerical Integration in Learning Dynamical Systems
di: Lu, Bing-Ze, et al.
Pubblicazione: (2025)

Learning Stochastic Dynamical Systems with Structured Noise
di: Guo, Ziheng, et al.
Pubblicazione: (2025)

Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
di: Zheng, Haoyang, et al.
Pubblicazione: (2025)

A Discrete Neural Operator with Adaptive Sampling for Surrogate Modeling of Parametric Transient Darcy Flows in Porous Media
di: Chen, Zhenglong, et al.
Pubblicazione: (2025)

ZNO: Stable Rational Neural Operators in the Z-Domain for Discrete-Time Dynamics
di: Zhu, Xianli, et al.
Pubblicazione: (2026)

Machine Learning-Based Nonlinear Nudging for Chaotic Dynamical Systems
di: Oh, Jaemin, et al.
Pubblicazione: (2025)

When do World Models Successfully Learn Dynamical Systems?
di: Ross, Edmund, et al.
Pubblicazione: (2025)

FLUID: Flow-based Unified Inference for Dynamics
di: Cui, Tiangang, et al.
Pubblicazione: (2026)

VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction
di: Cao, Yadi, et al.
Pubblicazione: (2024)

A Hybrid Kernel-Free Boundary Integral Method with Operator Learning for Solving Parametric Partial Differential Equations In Complex Domains
di: Ling, Shuo, et al.
Pubblicazione: (2024)

On the Limits of Interpretable Machine Learning in Quintic Root Classification
di: Thomas, Rohan, et al.
Pubblicazione: (2026)

Temporal Difference Learning for High-Dimensional PIDEs with Jumps
di: Lu, Liwei, et al.
Pubblicazione: (2023)

A Deep Learning Framework for Multi-Operator Learning: Architectures and Approximation Theory
di: Weihs, Adrien, et al.
Pubblicazione: (2025)

Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
di: Sun, Jingmin, et al.
Pubblicazione: (2024)

Self-Supervised Temporal Super-Resolution of Energy Data using Generative Adversarial Transformer
di: Mu, Xuanhao, et al.
Pubblicazione: (2025)