Deep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Ganguly, Abhisek, Ansumali, Santosh, Succi, Sauro |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Kinetic-based regularization: Learning spatial derivatives and PDE applications
by: Ganguly, Abhisek, et al.
Published: (2026)
by: Ganguly, Abhisek, et al.
Published: (2026)
Randomness and signal propagation in physics-informed neural networks (PINNs): A neural PDE perspective
by: Tucny, Jean-Michel, et al.
Published: (2025)
by: Tucny, Jean-Michel, et al.
Published: (2025)
A kinetic-based regularization method for data science applications
by: Ganguly, Abhisek, et al.
Published: (2025)
by: Ganguly, Abhisek, et al.
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)
Chatbots and Zero Sales Resistance
by: Succi, Sauro
Published: (2024)
by: Succi, Sauro
Published: (2024)
Physics-Informed Graph Neural Networks for Water Distribution Systems
by: Ashraf, Inaam, et al.
Published: (2024)
by: Ashraf, Inaam, et al.
Published: (2024)
Learning Dynamics of Deep Learning -- Force Analysis of Deep Neural Networks
by: Ren, Yi
Published: (2025)
by: Ren, Yi
Published: (2025)
Proximity-Informed Calibration for Deep Neural Networks
by: Xiong, Miao, et al.
Published: (2023)
by: Xiong, Miao, et al.
Published: (2023)
Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts
by: Wang, Kun, et al.
Published: (2024)
by: Wang, Kun, et al.
Published: (2024)
SeqBattNet: A Discrete-State Physics-Informed Neural Network with Aging Adaptation for Battery Modeling
by: Tran, Khoa, et al.
Published: (2025)
by: Tran, Khoa, et al.
Published: (2025)
Complex Physics-Informed Neural Network
by: Si, Chenhao, et al.
Published: (2025)
by: Si, Chenhao, et al.
Published: (2025)
Physics-Informed Neural Networks and Extensions
by: Raissi, Maziar, et al.
Published: (2024)
by: Raissi, Maziar, et al.
Published: (2024)
Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning
by: Pirayeshshirazinezhad, Reza
Published: (2026)
by: Pirayeshshirazinezhad, Reza
Published: (2026)
Densely Multiplied Physics Informed Neural Networks
by: Jiang, Feilong, et al.
Published: (2024)
by: Jiang, Feilong, et al.
Published: (2024)
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)
Wormhole Dynamics in Deep Neural Networks
by: Lai, Yen-Lung, et al.
Published: (2025)
by: Lai, Yen-Lung, et al.
Published: (2025)
DIM: Enforcing Domain-Informed Monotonicity in Deep Neural Networks
by: Salim, Joshua, et al.
Published: (2025)
by: Salim, Joshua, et al.
Published: (2025)
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)
Interpretable Neural System Dynamics: Combining Deep Learning with System Dynamics Modeling to Support Critical Applications
by: D'Elia, Riccardo
Published: (2025)
by: D'Elia, Riccardo
Published: (2025)
Restricted Bayesian Neural Network
by: Ganguly, Sourav, et al.
Published: (2024)
by: Ganguly, Sourav, et al.
Published: (2024)
A note on the physical interpretation of neural PDE's
by: Succi, Sauro
Published: (2025)
by: Succi, Sauro
Published: (2025)
Hard-Constrained Neural Networks with Physics-Embedded Architecture for Residual Dynamics Learning and Invariant Enforcement in Cyber-Physical Systems
by: Spotorno, Enzo Nicolás, et al.
Published: (2025)
by: Spotorno, Enzo Nicolás, et al.
Published: (2025)
On the Impact of Class Imbalance on the Learning Dynamics of Deep Neural Networks:An Intuitive Insight
by: Mustapha, Ismail B., et al.
Published: (2026)
by: Mustapha, Ismail B., et al.
Published: (2026)
Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks
by: An, Kang, et al.
Published: (2026)
by: An, Kang, et al.
Published: (2026)
Improved Training of Physics-Informed Neural Networks with Model Ensembles
by: Haitsiukevich, Katsiaryna, et al.
Published: (2022)
by: Haitsiukevich, Katsiaryna, et al.
Published: (2022)
Learning from Integral Losses in Physics Informed Neural Networks
by: Saleh, Ehsan, et al.
Published: (2023)
by: Saleh, Ehsan, et al.
Published: (2023)
Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks
by: Mazandarani, Mehran, et al.
Published: (2025)
by: Mazandarani, Mehran, et al.
Published: (2025)
Learning Physics Informed Neural ODEs With Partial Measurements
by: Ghanem, Paul, et al.
Published: (2024)
by: Ghanem, Paul, et al.
Published: (2024)
Evidential Physics-Informed Neural Networks
by: Tan, Hai Siong, et al.
Published: (2025)
by: Tan, Hai Siong, et al.
Published: (2025)
Supervised Dynamic Dimension Reduction with Deep Neural Network
by: Luo, Zhanye, et al.
Published: (2025)
by: Luo, Zhanye, et al.
Published: (2025)
Dual Computational Horizons: Incompleteness and Unpredictability in Intelligent Systems
by: Ganguly, Abhisek
Published: (2025)
by: Ganguly, Abhisek
Published: (2025)
Physics-Assisted and Topology-Informed Deep Learning for Weather Prediction
by: Zheng, Jiaqi, et al.
Published: (2025)
by: Zheng, Jiaqi, et al.
Published: (2025)
Multi-Objective Loss Balancing for Physics-Informed Deep Learning
by: Bischof, Rafael, et al.
Published: (2021)
by: Bischof, Rafael, et al.
Published: (2021)
Physics-Informed Neural Network Surrogate Models for River Stage Prediction
by: Zoch, Maximilian, et al.
Published: (2025)
by: Zoch, Maximilian, et al.
Published: (2025)
Exploring Physics-Informed Neural Networks for Crop Yield Loss Forecasting
by: Miranda, Miro, et al.
Published: (2024)
by: Miranda, Miro, et al.
Published: (2024)
GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks
by: Ranasinghe, Nisal, et al.
Published: (2024)
by: Ranasinghe, Nisal, et al.
Published: (2024)
Meta-Learning and Knowledge Discovery based Physics-Informed Neural Network for Remaining Useful Life Prediction
by: Wang, Yu, et al.
Published: (2025)
by: Wang, Yu, et al.
Published: (2025)
Physics-Informed Deep Learning for Entropy Prediction in Heterogeneous Systems: Thermodynamic and Information-Theoretic Case Studies
by: Sahoo, Biswajeet, et al.
Published: (2026)
by: Sahoo, Biswajeet, et al.
Published: (2026)
HorNets: Learning from Discrete and Continuous Signals with Routing Neural Networks
by: Koloski, Boshko, et al.
Published: (2025)
by: Koloski, Boshko, et al.
Published: (2025)
Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
by: Zheng, Zinan, et al.
Published: (2024)
by: Zheng, Zinan, et al.
Published: (2024)
Similar Items
-
Kinetic-based regularization: Learning spatial derivatives and PDE applications
by: Ganguly, Abhisek, et al.
Published: (2026) -
Randomness and signal propagation in physics-informed neural networks (PINNs): A neural PDE perspective
by: Tucny, Jean-Michel, et al.
Published: (2025) -
A kinetic-based regularization method for data science applications
by: Ganguly, Abhisek, et al.
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) -
Chatbots and Zero Sales Resistance
by: Succi, Sauro
Published: (2024)