Image de couverture de livre

Recovering Physical Dynamics from Discrete Observations via Intrinsic Differential Consistency

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Luo, Yuxiang, Perrault, Andrew
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales
par: Byun, Ju-Seung, et autres
Publié: (2024)

Normality-Guided Distributional Reinforcement Learning for Continuous Control
par: Byun, Ju-Seung, et autres
Publié: (2022)

Bridging Discrete Marks and Continuous Dynamics: Dual-Path Cross-Interaction for Marked Temporal Point Processes
par: Liu, Yuxiang, et autres
Publié: (2026)

Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize
par: Shah, Sanket, et autres
Publié: (2023)

DLPO: Diffusion Model Loss-Guided Reinforcement Learning for Fine-Tuning Text-to-Speech Diffusion Models
par: Chen, Jingyi, et autres
Publié: (2024)

StructRL: Recovering Dynamic Programming Structure from Learning Dynamics in Distributional Reinforcement Learning
par: Nowak, Ivo
Publié: (2026)

LaWM: Least Action World Models for Long-Horizon Physical Consistency from Visual Observations
par: Xiao, Qixin, et autres
Publié: (2026)

Optimizing Urban Service Allocation with Time-Constrained Restless Bandits
par: Mao, Yi, et autres
Publié: (2025)

naPINN: Noise-Adaptive Physics-Informed Neural Networks for Recovering Physics from Corrupted Measurement
par: Kim, Hankyeol, et autres
Publié: (2026)

An Operator-Consistent Graph Neural Network for Learning Diffusion Dynamics on Irregular Meshes
par: Li, Yuelian, et autres
Publié: (2025)

Gauge-Equivariant Intrinsic Neural Operators for Geometry-Consistent Learning of Elliptic PDE Maps
par: Cheng, Pengcheng
Publié: (2026)

Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning
par: Schlaginhaufen, Andreas, et autres
Publié: (2024)

Implicit Neural Differential Model for Spatiotemporal Dynamics
par: Akhare, Deepak, et autres
Publié: (2025)

ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI Feedback
par: Byun, Ju-Seung, et autres
Publié: (2024)

Physics-Informed Neural Networks for Vessel Trajectory Prediction: Learning Time-Discretized Kinematic Dynamics via Finite Differences
par: Alam, Md Mahbub, et autres
Publié: (2025)

Deep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed Learning
par: Ganguly, Abhisek, et autres
Publié: (2026)

Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning
par: Yang, Hanlin, et autres
Publié: (2024)

Scale-Consistent State-Space Dynamics via Fractal of Stationary Transformations
par: Yu, Geunhyeok, et autres
Publié: (2026)

MIR: Efficient Exploration in Episodic Multi-Agent Reinforcement Learning via Mutual Intrinsic Reward
par: Chen, Kesheng, et autres
Publié: (2025)

Scalable Machines with Intrinsic Higher Mental-State Dynamics
par: Adeel, Ahsan, et autres
Publié: (2026)

Recovering Manifold Structure Using Ollivier-Ricci Curvature
par: Saidi, Tristan Luca, et autres
Publié: (2024)

Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots
par: Deng, Boyuan, et autres
Publié: (2025)

On the Dynamics of Observation and Semantics
par: Li, Xiu
Publié: (2026)

SCL-GNN: Towards Generalizable Graph Neural Networks via Spurious Correlation Learning
par: Zhang, Yuxiang, et autres
Publié: (2026)

Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?
par: Blum, Avrim, et autres
Publié: (2019)

Sample from What You See: Visuomotor Policy Learning via Diffusion Bridge with Observation-Embedded Stochastic Differential Equation
par: Liu, Zhaoyang, et autres
Publié: (2025)

GIFT: Global stabilisation via Intrinsic Fine Tuning
par: Young, Rory, et autres
Publié: (2026)

Intrinsically Interpretable Attention via Sparse Post-Training
par: Draye, Florent, et autres
Publié: (2025)

Self-ReSET: Learning to Self-Recover from Unsafe Reasoning Trajectories
par: Zhang, Dongcheng, et autres
Publié: (2026)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
par: Rector-Brooks, Jarrid, et autres
Publié: (2024)

Pro-KLShampoo: Projected KL-Shampoo with Whitening Recovered by Orthogonalization
par: Sun, Ruotong, et autres
Publié: (2026)

Recovering implicit physics model under real-world constraints
par: Banerjee, Ayan, et autres
Publié: (2024)

Discrete World Models via Regularization
par: Bizzaro, Davide, et autres
Publié: (2026)

Anomaly Detection in Complex Dynamical Systems: A Systematic Framework Using Embedding Theory and Physics-Inspired Consistency
par: Somma, Michael, et autres
Publié: (2025)

Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems
par: Somma, Michael
Publié: (2025)

On Recovering Higher-order Interactions from Protein Language Models
par: Tsui, Darin, et autres
Publié: (2024)

Verifier-Free RL for LLMs via Intrinsic Gradient-Norm Reward
par: Wen, Xuexiang, et autres
Publié: (2026)

Discrete Semantic States and Hamiltonian Dynamics in LLM Embedding Spaces
par: Laine, Timo Aukusti
Publié: (2025)

Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces
par: Akkerman, Fabian, et autres
Publié: (2023)

Physical Consistency of Aurora's Encoder: A Quantitative Study
par: Richards, Benjamin, et autres
Publié: (2025)