Imagen de Portada

PHAST: Port-Hamiltonian Architecture for Structured Temporal Dynamics Forecasting

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Bhardwaj, Shubham, Bajaj, Chandrajit
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Systems and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series
por: Correia, Lucas, et al.
Publicado: (2024)

Foundation Models for the Electric Power Grid
por: Hamann, Hendrik F., et al.
Publicado: (2024)

Scenario-based Thermal Management Parametrization Through Deep Reinforcement Learning
por: Rudolf, Thomas, et al.
Publicado: (2024)

PIRF: Physics-Informed Reward Fine-Tuning for Diffusion Models
por: Yuan, Mingze, et al.
Publicado: (2025)

Simulation-Based Validation of an Integrated 4D/5D Digital-Twin Framework for Predictive Construction Control
por: Khoshkonesh, Atena, et al.
Publicado: (2025)

Controlling Large Electric Vehicle Charging Stations via User Behavior Modeling and Stochastic Programming
por: Puech, Alban, et al.
Publicado: (2024)

Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms
por: Zhang, Xiangyuan, et al.
Publicado: (2023)

PDE Control Gym: A Benchmark for Data-Driven Boundary Control of Partial Differential Equations
por: Bhan, Luke, et al.
Publicado: (2024)

Harnessing Flexible Spatial and Temporal Data Center Workloads for Grid Regulation Services
por: Fan, Yingrui, et al.
Publicado: (2026)

SINDyG: Sparse Identification of Nonlinear Dynamical Systems from Graph-Structured Data, with Applications to Stuart-Landau Oscillator Networks
por: Basiri, Mohammad Amin, et al.
Publicado: (2024)

SciNav: A General Agent Framework for Scientific Coding Tasks
por: Zhang, Tianshu, et al.
Publicado: (2026)

Low-cost Robust Night-time Aerial Material Segmentation through Hyperspectral Data and Sparse Spatio-Temporal Learning
por: Bajaj, Chandrajit, et al.
Publicado: (2024)

CauSTream: Causal Spatio-Temporal Representation Learning for Streamflow Forecasting
por: Wan, Shu, et al.
Publicado: (2025)

Port-Hamiltonian formulation and structure-preserving discretization of hyperelastic strings
por: Kinon, Philipp L., et al.
Publicado: (2023)

Improving Surrogate Model Robustness to Perturbations for Dynamical Systems Through Machine Learning and Data Assimilation
por: Ajayakumar, Abhishek, et al.
Publicado: (2023)

Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review
por: Lee, Suk Ki, et al.
Publicado: (2025)

Natural-gas storage modelling by deep reinforcement learning
por: Balaconi, Tiziano, et al.
Publicado: (2025)

Hidformer: Transformer-Style Neural Network in Stock Price Forecasting
por: Szydłowski, Kamil Ł., et al.
Publicado: (2024)

XGBoost Learning of Dynamic Wager Placement for In-Play Betting on an Agent-Based Model of a Sports Betting Exchange
por: Terawong, Chawin, et al.
Publicado: (2024)

Graph Neural Networks for Virtual Sensing in Complex Systems: Addressing Heterogeneous Temporal Dynamics
por: Zhao, Mengjie, et al.
Publicado: (2024)

Design-OS: A Specification-Driven Framework for Engineering System Design with a Control-Systems Design Case
por: Bank, H. Sinan, et al.
Publicado: (2026)

A Toolbox for Supporting Research on AI in Water Distribution Networks
por: Artelt, André, et al.
Publicado: (2024)

Designing MacPherson Suspension Architectures using Bayesian Optimization
por: Thomas, Sinnu Susan, et al.
Publicado: (2022)

UrbanGraph: Physics-Informed Spatio-Temporal Dynamic Heterogeneous Graphs for Urban Microclimate Prediction
por: Xin, Weilin, et al.
Publicado: (2025)

Bubbleformer: Forecasting Boiling with Transformers
por: Hassan, Sheikh Md Shakeel, et al.
Publicado: (2025)

Exploring the non-convexity in machine learning using quantum-inspired optimization
por: Kumar, Kandula Eswara Sai, et al.
Publicado: (2026)

A data-driven framework for team selection in Fantasy Premier League
por: Ramezani, Danial, et al.
Publicado: (2025)

Weighted Flow Matching and Physics-Informed Nonlinear Filtering for Parameter Estimation in Digital Twins
por: Yanik, Yasar, et al.
Publicado: (2026)

Blue and Green-Mode Energy-Efficient Nanoparticle-Based Chemiresistive Sensor Array Realized by Rapid Ensemble Learning
por: Wang, Zeheng, et al.
Publicado: (2024)

Reliability comparison of vessel trajectory prediction models via Probability of Detection
por: Rastin, Zahra, et al.
Publicado: (2025)

System Identification of a Moored ASV with Recessed Moon Pool via Deterministic and Bayesian Hankel-DMDc
por: Palma, Giorgio, et al.
Publicado: (2025)

Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries
por: Tu, Hao, et al.
Publicado: (2021)

C-ShipGen: A Conditional Guided Diffusion Model for Parametric Ship Hull Design
por: Bagazinski, Noah J., et al.
Publicado: (2024)

Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models
por: Lemmel, Julian, et al.
Publicado: (2025)

EngiBench: A Framework for Data-Driven Engineering Design Research
por: Felten, Florian, et al.
Publicado: (2025)

Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points
por: Esders, Malte, et al.
Publicado: (2023)

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

Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks
por: Zhang, Zhiyu, et al.
Publicado: (2024)

Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)
por: Leung, Sze Chai, et al.
Publicado: (2025)

Efficient Training of Learning-Based Thermal Power Flow for 4th Generation District Heating Grids
por: Bott, Andreas, et al.
Publicado: (2024)