Physics-Informed Neural Learning for State Reconstruction and Parameter Identification in Coupled Greenhouse Climate Dynamics
Fuente:
arXiv
Saved in:
| Main Authors: | Biswas, Sani, Ansari, Khursheed J., Akhtar, Md. Nasim |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks
by: Nasim, Imran, et al.
Published: (2024)
by: Nasim, Imran, et al.
Published: (2024)
Dynamic Reconstruction of Ultrasound-Derived Flow Fields With Physics-Informed Neural Fields
by: Patel, Viraj, et al.
Published: (2025)
by: Patel, Viraj, et al.
Published: (2025)
PIDM-DP: Physics-Informed Diffusion with Dormand-Prince Integration for Chaotic System Identification and State Reconstruction across Multiple Dynamical Regimes
by: Dabral, Shailendra
Published: (2026)
by: Dabral, Shailendra
Published: (2026)
Grower-in-the-Loop Interactive Reinforcement Learning for Greenhouse Climate Control
by: Xiao, Maxiu, et al.
Published: (2025)
by: Xiao, Maxiu, et al.
Published: (2025)
Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks
by: Haywood-Alexander, Marcus, et al.
Published: (2024)
by: Haywood-Alexander, Marcus, et al.
Published: (2024)
Non-Invasive Reconstruction of Cardiac Activation Dynamics Using Physics-Informed Neural Networks
by: Dermul, Nathan, et al.
Published: (2026)
by: Dermul, Nathan, et al.
Published: (2026)
Parameter Identification for Partial Differential Equation with Jump Discontinuities in Coefficients by Markov Switching Model and Physics-Informed Machine Learning
by: Zhang, Zhikun, et al.
Published: (2025)
by: Zhang, Zhikun, et al.
Published: (2025)
Time-Scale Coupling Between States and Parameters in Recurrent Neural Networks
by: Livi, Lorenzo
Published: (2025)
by: Livi, Lorenzo
Published: (2025)
Physics-Informed Neural Networks for Vessel Trajectory Prediction: Learning Time-Discretized Kinematic Dynamics via Finite Differences
by: Alam, Md Mahbub, et al.
Published: (2025)
by: Alam, Md Mahbub, et al.
Published: (2025)
Physics-Guided Concentration Inference from Resistance Transients in a Mixed-Phase SnO-SnO$_2$ Carbon Monoxide Sensor with p-n Switching
by: Biswas, Sani, et al.
Published: (2026)
by: Biswas, Sani, et al.
Published: (2026)
Physics-Informed Neural Nets for Control of Dynamical Systems
by: Antonelo, Eric Aislan, et al.
Published: (2021)
by: Antonelo, Eric Aislan, et al.
Published: (2021)
A Comprehensive Review on Understanding the Decentralized and Collaborative Approach in Machine Learning
by: Saif, Sarwar, et al.
Published: (2025)
by: Saif, Sarwar, et al.
Published: (2025)
Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems
by: Jahin, Md Abrar, et al.
Published: (2025)
by: Jahin, Md Abrar, et al.
Published: (2025)
Physics-Informed Neural Networks for Joint Source and Parameter Estimation in Advection-Diffusion Equations
by: Anague, Brenda, et al.
Published: (2025)
by: Anague, Brenda, et al.
Published: (2025)
Unsupervised Deep Clustering of MNIST with Triplet-Enhanced Convolutional Autoencoders
by: Ansari, Md. Faizul Islam
Published: (2025)
by: Ansari, Md. Faizul Islam
Published: (2025)
Modeling COVID-19 Dynamics in German States Using Physics-Informed Neural Networks
by: Rothenbeck, Phillip, et al.
Published: (2025)
by: Rothenbeck, Phillip, et al.
Published: (2025)
Dissipative Latent Residual Physics-Informed Neural Networks for Modeling and Identification of Electromechanical Systems
by: Long, Youyuan, et al.
Published: (2026)
by: Long, Youyuan, et al.
Published: (2026)
Robust Parameter and State Estimation in Multiscale Neuronal Systems Using Physics-Informed Neural Networks
by: Wei, Changliang, et al.
Published: (2026)
by: Wei, Changliang, et al.
Published: (2026)
Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems
by: Nasim, Imran, et al.
Published: (2024)
by: Nasim, Imran, et al.
Published: (2024)
Variational Physics-Informed Ansatz for Reconstructing Hidden Interaction Networks from Steady States
by: Luo, Kaiming
Published: (2025)
by: Luo, Kaiming
Published: (2025)
Novel Deep Learning Architecture for Heart Disease Prediction using Convolutional Neural Network
by: Hussain, Shadab, et al.
Published: (2021)
by: Hussain, Shadab, et al.
Published: (2021)
Learning Transferable Friction Models and LuGre Identification Via Physics-Informed Neural Networks
by: Ozmen, Asutay, et al.
Published: (2025)
by: Ozmen, Asutay, et al.
Published: (2025)
Deep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed Learning
by: Ganguly, Abhisek, et al.
Published: (2026)
by: Ganguly, Abhisek, et al.
Published: (2026)
Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data
by: Velioglu, Mehmet, et al.
Published: (2024)
by: Velioglu, Mehmet, et al.
Published: (2024)
Ga$_2$O$_3$ TCAD Mobility Parameter Calibration using Simulation Augmented Machine Learning with Physics Informed Neural Network
by: Nguyen, Le Minh Long, et al.
Published: (2025)
by: Nguyen, Le Minh Long, et al.
Published: (2025)
Learning Hybrid Dynamics Models With Simulator-Informed Latent States
by: Ensinger, Katharina, et al.
Published: (2023)
by: Ensinger, Katharina, et al.
Published: (2023)
Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction
by: Zhang, Huiwen, et al.
Published: (2026)
by: Zhang, Huiwen, et al.
Published: (2026)
Symmetry-Reduced Physics-Informed Learning of Tensegrity Dynamics
by: Qin, Jing, et al.
Published: (2026)
by: Qin, Jing, et al.
Published: (2026)
Physics-Informed Neural ODEs with Scale-Aware Residuals for Learning Stiff Biophysical Dynamics
by: Kainth, Kamalpreet Singh, et al.
Published: (2025)
by: Kainth, Kamalpreet Singh, et al.
Published: (2025)
Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks
by: Song, Siyuan, et al.
Published: (2023)
by: Song, Siyuan, et al.
Published: (2023)
A Physics Informed Machine Learning Framework for Optimal Sensor Placement and Parameter Estimation
by: Venianakis, Georgios, et al.
Published: (2025)
by: Venianakis, Georgios, et al.
Published: (2025)
Replay-Based Continual Learning for Physics-Informed Neural Operators
by: Wang, Yizheng, et al.
Published: (2026)
by: Wang, Yizheng, et al.
Published: (2026)
Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning
by: Nath, Pritthijit, et al.
Published: (2026)
by: Nath, Pritthijit, et al.
Published: (2026)
Physics-Informed Regression: Parameter Estimation in Parameter-Linear Nonlinear Dynamic Models
by: Nielsen, Jonas Søeborg, et al.
Published: (2025)
by: Nielsen, Jonas Søeborg, et al.
Published: (2025)
Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data
by: Chu, Mengyu, et al.
Published: (2022)
by: Chu, Mengyu, et al.
Published: (2022)
Automated Manifold Learning for Reduced Order Modeling
by: Nasim, Imran, et al.
Published: (2025)
by: Nasim, Imran, et al.
Published: (2025)
A Hybrid Surrogate for Electric Vehicle Parameter Estimation and Power Consumption via Physics-Informed Neural Operators
by: Lim, Hansol, et al.
Published: (2025)
by: Lim, Hansol, et al.
Published: (2025)
A Physics Informed Neural Network (PINN) Methodology for Coupled Moving Boundary PDEs
by: Kathane, Shivprasad, et al.
Published: (2024)
by: Kathane, Shivprasad, et al.
Published: (2024)
Quantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems
by: Jahin, Md Abrar, et al.
Published: (2025)
by: Jahin, Md Abrar, et al.
Published: (2025)
BRAID: Input-Driven Nonlinear Dynamical Modeling of Neural-Behavioral Data
by: Vahidi, Parsa, et al.
Published: (2025)
by: Vahidi, Parsa, et al.
Published: (2025)
Similar Items
-
Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks
by: Nasim, Imran, et al.
Published: (2024) -
Dynamic Reconstruction of Ultrasound-Derived Flow Fields With Physics-Informed Neural Fields
by: Patel, Viraj, et al.
Published: (2025) -
PIDM-DP: Physics-Informed Diffusion with Dormand-Prince Integration for Chaotic System Identification and State Reconstruction across Multiple Dynamical Regimes
by: Dabral, Shailendra
Published: (2026) -
Grower-in-the-Loop Interactive Reinforcement Learning for Greenhouse Climate Control
by: Xiao, Maxiu, et al.
Published: (2025) -
Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks
by: Haywood-Alexander, Marcus, et al.
Published: (2024)