Cover Image

A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Xuyang, Harlim, John, Chakraborty, Dibyajyoti, Maulik, Romit
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Dynamical Systems
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Binned Spectral Power Loss for Improved Prediction of Chaotic Systems
by: Chakraborty, Dibyajyoti, et al.
Published: (2025)

Deep Learning of Solver-Aware Turbulence Closures from Nudged LES Dynamics
by: Suriyanarayanan, Ashwin, et al.
Published: (2026)

Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems
by: Chakraborty, Dibyajyoti, et al.
Published: (2026)

Divide And Conquer: Learning Chaotic Dynamical Systems With Multistep Penalty Neural Ordinary Differential Equations
by: Chakraborty, Dibyajyoti, et al.
Published: (2024)

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

Differentiable Turbulence: Closure as a partial differential equation constrained optimization
by: Shankar, Varun, et al.
Published: (2023)

A note on the error analysis of data-driven closure models for large eddy simulations of turbulence
by: Chakraborty, Dibyajyoti, et al.
Published: (2024)

Higher order quantum reservoir computing for non-intrusive reduced-order models
by: Jain, Vinamr, et al.
Published: (2024)

A competitive baseline for deep learning enhanced data assimilation using conditional Gaussian ensemble Kalman filtering
by: Malik, Zachariah, et al.
Published: (2024)

Measure-Theoretic Time-Delay Embedding
by: Botvinick-Greenhouse, Jonah, et al.
Published: (2024)

SC3D: Dynamic and Differentiable Causal Discovery for Temporal and Instantaneous Graphs
by: Das, Sourajit, et al.
Published: (2026)

Interpretable Diagnostics and Adaptive Data Assimilation for Neural ODEs via Discrete Empirical Interpolation
by: Kim, Hojin, et al.
Published: (2025)

Learning the Simplest Neural ODE
by: Okamoto, Yuji, et al.
Published: (2025)

FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models
by: Raut, Riddhiman, et al.
Published: (2025)

SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning
by: Li, Xuyang, et al.
Published: (2025)

Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models
by: Barwey, Shivam, et al.
Published: (2023)

DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems
by: Schiff, Yair, et al.
Published: (2024)

Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems
by: Shokar, Ira J. S., et al.
Published: (2025)

MPINeuralODE: Multiple-Initial-Condition Physics-Informed Neural ODEs for Globally Consistent Dynamical System Learning
by: Yang, Lake, et al.
Published: (2026)

Generalizable data-driven turbulence closure modeling on unstructured grids with differentiable physics
by: Kim, Hojin, et al.
Published: (2023)

Teacher Forcing as Generalized Bayes: Optimization Geometry Mismatch in Switching Surrogates for Chaotic Dynamics
by: Herz, Andre, et al.
Published: (2026)

Conditioning on PDE Parameters to Generalise Deep Learning Emulation of Stochastic and Chaotic Dynamics
by: Shokar, Ira J. S., et al.
Published: (2025)

A Physics-informed Machine Learning-based Control Method for Nonlinear Dynamic Systems with Highly Noisy Measurements
by: Ma, Mason, et al.
Published: (2023)

A Bayesian Framework for Symmetry Inference in Chaotic Attractors
by: Ghanem, Ziad, et al.
Published: (2025)

Machine-Precision Prediction of Low-Dimensional Chaotic Systems
by: Schötz, Christof, et al.
Published: (2025)

Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs
by: Krach, Florian, et al.
Published: (2024)

Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series
by: Panja, Madhurima, et al.
Published: (2026)

Principal Component Flow Map Learning of PDEs from Incomplete, Limited, and Noisy Data
by: Churchill, Victor
Published: (2024)

Neural Context Flows for Meta-Learning of Dynamical Systems
by: Nzoyem, Roussel Desmond, et al.
Published: (2024)

Predicting Chaotic System Behavior using Machine Learning Techniques
by: Rao, Huaiyuan, et al.
Published: (2024)

Horizon-Constrained Rashomon Sets for Chaotic Forecasting
by: Kale, Gauri, et al.
Published: (2026)

Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics
by: Samota, Sandeep Kumar, et al.
Published: (2026)

Joint Learning of Linear Time-Invariant Dynamical Systems
by: Modi, Aditya, et al.
Published: (2021)

Neural Ordinary Differential Equations for Learning and Extrapolating System Dynamics Across Bifurcations
by: van Tegelen, Eva, et al.
Published: (2025)

Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective
by: Yang, Sunwoong, et al.
Published: (2024)

Designing Chaotic Attractors: A Semi-supervised Approach
by: Kabayama, Tempei, et al.
Published: (2024)

A Novel Neural Filter to Improve Accuracy of Neural Network Models of Dynamic Systems
by: Oveissi, Parham, et al.
Published: (2024)

Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data
by: Brenner, Manuel, et al.
Published: (2024)

Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows
by: Ozan, Defne E., et al.
Published: (2025)

Adaptive Nonlinear Vector Autoregression: Robust Forecasting for Noisy Chaotic Time Series
by: Sherkhon, Azimov, et al.
Published: (2025)