Cover Image

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	McLennan, Luke, Wang, Yi, Farell, Ryan, Nguyen, Minh, Bajaj, Chandrajit
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes Learning
by: Bajaj, Chandrajit, et al.
Published: (2024)

When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
by: Wang, Yi, et al.
Published: (2026)

GRL-SNAM: Geometric Reinforcement Learning with Path Differential Hamiltonians for Simultaneous Navigation and Mapping in Unknown Environments
by: Ellendula, Aditya Sai, et al.
Published: (2025)

A Differential and Pointwise Control Approach to Reinforcement Learning
by: Nguyen, Minh, et al.
Published: (2024)

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

Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation
by: Ellendula, Aditya Sai, et al.
Published: (2026)

PHAST: Port-Hamiltonian Architecture for Structured Temporal Dynamics Forecasting
by: Bhardwaj, Shubham, et al.
Published: (2026)

Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach
by: Bajaj, Chandrajit, et al.
Published: (2022)

Self-Balancing, Memory Efficient, Dynamic Metric Space Data Maintenance, for Rapid Multi-Kernel Estimation
by: Ellendula, Aditya S, et al.
Published: (2025)

Physics-informed neural networks via stochastic Hamiltonian dynamics learning
by: Bajaj, Chandrajit, et al.
Published: (2021)

Sample Efficient Learning of Factored Embeddings of Tensor Fields
by: Heo, Taemin, et al.
Published: (2022)

Scalable Robust Bayesian Co-Clustering with Compositional ELBOs
by: Vinod, Ashwin, et al.
Published: (2025)

Machine Learning Hamiltonian Dynamical Systems with Sparse and Noisy Data
by: Thapar, Vedanta, et al.
Published: (2026)

Queryable LoRA: Instruction-Regularized Routing Over Shared Low-Rank Update Atoms
by: Vaidya, Omatharv Bharat, et al.
Published: (2026)

Decoupled Continuous-Time Reinforcement Learning via Hamiltonian Flow
by: Nguyen, Minh
Published: (2026)

Parque Nacional de Makalu-Barun, Nepal
by: McLennan, Marty

Parque Nacional y Parques Estatales de Rodwood, Estados Unidos
by: McLennan, Marty

Parque Nacional Mesa Verde, Estados Unidos
by: McLennan, Marty

Parque Nacional de Fuji-Hakone-Izu, Japón
by: McLennan, Marty

Parque Nacional Gaspésie, Canadá
by: McLennan, Marty

Reserva de la Biosfera Sian Ka'an, México
by: McLennan, Marty

Parque Nacional Göreme, Turquía
by: McLennan, Marty

Parque Nacional de Gunung Mulu, Borneo
by: McLennan, Marty

Partnering for Health Workforce Development: A Case Study of Australian Volunteers International and the Solomon Islands Medical Internship Training Programme
by: Sharon McLennan
Published: (2026)

Structure- and Stability-Preserving Learning of Port-Hamiltonian Systems
by: Nguyen, Binh, et al.
Published: (2026)

Detecting and Rectifying Noisy Labels: A Similarity-based Approach
by: Huu-Tien, Dang, et al.
Published: (2025)

Understanding SAM's Robustness to Noisy Labels through Gradient Down-weighting
by: Luong, Hoang-Chau, et al.
Published: (2024)

Learning Trajectories of Hamiltonian Systems with Neural Networks
by: Haitsiukevich, Katsiaryna, et al.
Published: (2022)

Learning from Noisy Labels via Conditional Distributionally Robust Optimization
by: Guo, Hui, et al.
Published: (2024)

Learning Hamiltonian Dynamics with Bayesian Data Assimilation
by: Kim, Taehyeun, et al.
Published: (2025)

Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
by: Ewering, Jan-Hendrik, et al.
Published: (2025)

Discovering Car-following Dynamics from Trajectory Data through Deep Learning
by: Angah, Ohay, et al.
Published: (2024)

Collaboratively Learning Federated Models from Noisy Decentralized Data
by: Li, Haoyuan, et al.
Published: (2024)

Metacognitive Sensitivity for Test-Time Dynamic Model Selection
by: Trinh, Le Tuan Minh, et al.
Published: (2025)

Generation from Noisy Examples
by: Raman, Ananth, et al.
Published: (2025)

Prioritizing Informative Features and Examples for Deep Learning from Noisy Data
by: Park, Dongmin
Published: (2024)

Sequence Diffusion Model for Temporal Link Prediction in Continuous-Time Dynamic Graph
by: Duc, Nguyen Minh, et al.
Published: (2026)

Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection
by: Kim, Suyeon, et al.
Published: (2024)

Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Modelling Approach
by: Nguyen, Cuong, et al.
Published: (2023)

Tree-Based Predictive Models for Noisy Input Data
by: McCoy, Kevin, et al.
Published: (2026)