Discovering Partially Known Ordinary Differential Equations: a Case Study on the Chemical Kinetics of Cellulose Degradation
Fuente:
arXiv
Saved in:
| Main Authors: | Bragone, Federica, Morozovska, Kateryna, Laneryd, Tor, Shukla, Khemraj, Markidis, Stefano |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Data-Driven vs Traditional Approaches to Power Transformer's Top-Oil Temperature Estimation
by: Tembo, Francis, et al.
Published: (2025)
by: Tembo, Francis, et al.
Published: (2025)
Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks
by: Li, Sirui, et al.
Published: (2025)
by: Li, Sirui, et al.
Published: (2025)
$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks
by: Figueres, Júlia Vicens, et al.
Published: (2025)
by: Figueres, Júlia Vicens, et al.
Published: (2025)
MILP initialization for solving parabolic PDEs with PINNs
by: Li, Sirui, et al.
Published: (2025)
by: Li, Sirui, et al.
Published: (2025)
Discovering Interpretable Ordinary Differential Equations from Noisy Data
by: Golder, Rahul, et al.
Published: (2025)
by: Golder, Rahul, et al.
Published: (2025)
Discovering Governing Equations of Geomagnetic Storm Dynamics with Symbolic Regression
by: Markidis, Stefano, et al.
Published: (2025)
by: Markidis, Stefano, et al.
Published: (2025)
Randomized Forward Mode of Automatic Differentiation For Optimization Algorithms
by: Shukla, Khemraj, et al.
Published: (2023)
by: Shukla, Khemraj, et al.
Published: (2023)
Hybrid Neural Ordinary Differential Equations for Data-Efficient Polymerization Modeling with Incomplete Kinetics
by: Almanasreh, Marah, et al.
Published: (2026)
by: Almanasreh, Marah, et al.
Published: (2026)
Discovering Ordinary Differential Equations with LLM-Based Qualitative and Quantitative Evaluation
by: Song, Sum Kyun, et al.
Published: (2026)
by: Song, Sum Kyun, et al.
Published: (2026)
Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations
by: Thöni, Anna C. M., et al.
Published: (2025)
by: Thöni, Anna C. M., et al.
Published: (2025)
Foundation Inference Models for Ordinary Differential Equations
by: Mauel, Maximilian, et al.
Published: (2026)
by: Mauel, Maximilian, et al.
Published: (2026)
Symbolic Neural Ordinary Differential Equations
by: Li, Xin, et al.
Published: (2025)
by: Li, Xin, et al.
Published: (2025)
Identifiability Challenges in Sparse Linear Ordinary Differential Equations
by: Casolo, Cecilia, et al.
Published: (2025)
by: Casolo, Cecilia, et al.
Published: (2025)
Post-Regularization Confidence Bands for Ordinary Differential Equations
by: Dai, Xiaowu, et al.
Published: (2021)
by: Dai, Xiaowu, et al.
Published: (2021)
Generalization Bound for a General Class of Neural Ordinary Differential Equations
by: Verma, Madhusudan, et al.
Published: (2025)
by: Verma, Madhusudan, et al.
Published: (2025)
Modeling Time Series Dynamics with Fourier Ordinary Differential Equations
by: Guo, Muhao, et al.
Published: (2025)
by: Guo, Muhao, et al.
Published: (2025)
A Posteriori Evaluation of a Physics-Constrained Neural Ordinary Differential Equations Approach Coupled with CFD Solver for Modeling Stiff Chemical Kinetics
by: Kumar, Tadbhagya, et al.
Published: (2023)
by: Kumar, Tadbhagya, et al.
Published: (2023)
Semi-Implicit Neural Ordinary Differential Equations
by: Zhang, Hong, et al.
Published: (2024)
by: Zhang, Hong, et al.
Published: (2024)
Forecasting N-Body Dynamics: A Comparative Study of Neural Ordinary Differential Equations and Universal Differential Equations
by: S, Suriya R, et al.
Published: (2025)
by: S, Suriya R, et al.
Published: (2025)
Discovering Symbolic Differential Equations with Symmetry Invariants
by: Yang, Jianke, et al.
Published: (2025)
by: Yang, Jianke, et al.
Published: (2025)
Neural Ordinary Differential Equations for Modeling Socio-Economic Dynamics
by: Samota, Sandeep Kumar, et al.
Published: (2026)
by: Samota, Sandeep Kumar, et al.
Published: (2026)
Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers
by: Li, Shi
Published: (2025)
by: Li, Shi
Published: (2025)
Drug Release Modeling using Physics-Informed Neural Networks
by: Qureshi, Daanish Aleem, et al.
Published: (2026)
by: Qureshi, Daanish Aleem, et al.
Published: (2026)
Grammar-based Ordinary Differential Equation Discovery
by: Yu, Karin L., et al.
Published: (2025)
by: Yu, Karin L., et al.
Published: (2025)
Stability-Informed Initialization of Neural Ordinary Differential Equations
by: Westny, Theodor, et al.
Published: (2023)
by: Westny, Theodor, et al.
Published: (2023)
Online Traffic Density Estimation using Physics-Informed Neural Networks
by: Wilkman, Dennis, et al.
Published: (2025)
by: Wilkman, Dennis, et al.
Published: (2025)
Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations
by: Song, Yujee, et al.
Published: (2024)
by: Song, Yujee, et al.
Published: (2024)
Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations
by: Beck, Jonas, et al.
Published: (2024)
by: Beck, Jonas, et al.
Published: (2024)
Uncertainty Propagation Networks for Neural Ordinary Differential Equations
by: Jahanshahi, Hadi, et al.
Published: (2025)
by: Jahanshahi, Hadi, et al.
Published: (2025)
Deep Operator Neural Network Model Predictive Control
by: de Jong, Thomas Oliver, et al.
Published: (2025)
by: de Jong, Thomas Oliver, et al.
Published: (2025)
Application of Neural Ordinary Differential Equations for ITER Burning Plasma Dynamics
by: Liu, Zefang, et al.
Published: (2024)
by: Liu, Zefang, et al.
Published: (2024)
Application of Neural Ordinary Differential Equations for Tokamak Plasma Dynamics Analysis
by: Liu, Zefang, et al.
Published: (2024)
by: Liu, Zefang, et al.
Published: (2024)
ICODEN: Ordinary Differential Equation Neural Networks for Interval-Censored Data
by: Wang, Haoling, et al.
Published: (2026)
by: Wang, Haoling, et al.
Published: (2026)
High-order expansion of Neural Ordinary Differential Equations flows
by: Izzo, Dario, et al.
Published: (2025)
by: Izzo, Dario, et al.
Published: (2025)
Discovering Physics-Informed Neural Networks Model for Solving Partial Differential Equations through Evolutionary Computation
by: Zhang, Bo, et al.
Published: (2024)
by: Zhang, Bo, et al.
Published: (2024)
Forecasting with an N-dimensional Langevin Equation and a Neural-Ordinary Differential Equation
by: Malpica-Morales, Antonio, et al.
Published: (2024)
by: Malpica-Morales, Antonio, et al.
Published: (2024)
Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation
by: Ding, Yanna, et al.
Published: (2024)
by: Ding, Yanna, et al.
Published: (2024)
Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations
by: Nagesh, Chandra Kanth, et al.
Published: (2025)
by: Nagesh, Chandra Kanth, et al.
Published: (2025)
Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketching
by: Jiang, Nan, et al.
Published: (2024)
by: Jiang, Nan, et al.
Published: (2024)
Neural Ordinary Differential Equations for Learning and Extrapolating System Dynamics Across Bifurcations
by: van Tegelen, Eva, et al.
Published: (2025)
by: van Tegelen, Eva, et al.
Published: (2025)
Similar Items
-
Data-Driven vs Traditional Approaches to Power Transformer's Top-Oil Temperature Estimation
by: Tembo, Francis, et al.
Published: (2025) -
Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks
by: Li, Sirui, et al.
Published: (2025) -
$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks
by: Figueres, Júlia Vicens, et al.
Published: (2025) -
MILP initialization for solving parabolic PDEs with PINNs
by: Li, Sirui, et al.
Published: (2025) -
Discovering Interpretable Ordinary Differential Equations from Noisy Data
by: Golder, Rahul, et al.
Published: (2025)