Copertina

Curriculum-Learned Vanishing Stacked Residual PINNs for Hyperbolic PDE State Reconstruction

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Eshkofti, Katayoun, Barreau, Matthieu
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Neural and Evolutionary Computing Machine Learning Systems and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Vanishing Stacked-Residual PINN for State Reconstruction of Hyperbolic Systems
di: Eshkofti, Katayoun, et al.
Pubblicazione: (2025)

Safe PDE Boundary Control with Neural Operators
di: Hu, Hanjiang, et al.
Pubblicazione: (2024)

SCAR: State-Space Compression for Scalable AI-Based Network Management of Vehicular Services
di: Comsa, Ioan-Sorin, et al.
Pubblicazione: (2025)

Real-Time Recurrent Reinforcement Learning
di: Lemmel, Julian, et al.
Pubblicazione: (2023)

Learning the optimal state-feedback via supervised imitation learning
di: Tailor, Dharmesh, et al.
Pubblicazione: (2019)

Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons
di: Ghoreishee, Aref, et al.
Pubblicazione: (2025)

Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids
di: Tiwari, Deepak, et al.
Pubblicazione: (2024)

Orthogonal Transforms in Neural Networks Amount to Effective Regularization
di: Zając, Krzysztof, et al.
Pubblicazione: (2023)

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems
di: Ashraf, Inaam, et al.
Pubblicazione: (2025)

Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics
di: da Silva, Samuel Filgueira, et al.
Pubblicazione: (2024)

Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm
di: Shahab, Muhammad Luthfi, et al.
Pubblicazione: (2026)

Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review
di: Nyangon, Joseph
Pubblicazione: (2026)

Improving the Performance of Echo State Networks Through State Feedback
di: Ehlers, Peter J., et al.
Pubblicazione: (2023)

Adaptive Control in Autonomous Driving via Real-Time Recurrent RL
di: Lemmel, Julian, et al.
Pubblicazione: (2026)

Control-Theoretic Techniques for Online Adaptation of Deep Neural Networks in Dynamical Systems
di: Elkins, Jacob G., et al.
Pubblicazione: (2024)

Guidance Design for Escape Flight Vehicle Using Evolution Strategy Enhanced Deep Reinforcement Learning
di: Hu, Xiao, et al.
Pubblicazione: (2024)

A Cloud-Edge Framework for Energy-Efficient Event-Driven Control: An Integration of Online Supervised Learning, Spiking Neural Networks and Local Plasticity Rules
di: Ahmadvand, Reza, et al.
Pubblicazione: (2024)

Designing Robust Software Sensors for Nonlinear Systems via Neural Networks and Adaptive Sliding Mode Control
di: Farkane, Ayoub, et al.
Pubblicazione: (2025)

Combining Large Language Models and Gradient-Free Optimization for Automatic Control Policy Synthesis
di: Bosio, Carlo, et al.
Pubblicazione: (2025)

Neural Control System for Continuous Glucose Monitoring and Maintenance
di: Wasi, Azmine Toushik
Pubblicazione: (2024)

Optimality Principles and Neural Ordinary Differential Equations-based Process Modeling for Distributed Control
di: Wartmann, Michael R., et al.
Pubblicazione: (2025)

Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems
di: Ruddick, Julian, et al.
Pubblicazione: (2024)

Application of Zone Method based Physics-Informed Neural Networks in Reheating Furnaces
di: Dutta, Ujjal Kr, et al.
Pubblicazione: (2023)

Controlling Chaotic Maps using Next-Generation Reservoir Computing
di: Kent, Robert M., et al.
Pubblicazione: (2023)

Context-Based Echo State Networks with Prediction Confidence for Human-Robot Shared Control
di: Amirshirzad, Negin, et al.
Pubblicazione: (2024)

Case Studies of Generative Machine Learning Models for Dynamical Systems
di: Bapat, Nachiket U., et al.
Pubblicazione: (2025)

Genesis: A Spiking Neuromorphic Accelerator With On-chip Continual Learning
di: Karia, Vedant, et al.
Pubblicazione: (2025)

Machine Learning Driven Global Optimisation Framework for Analog Circuit Design
di: Rashid, Ria, et al.
Pubblicazione: (2024)

Stochastic Reservoir Computers
di: Ehlers, Peter J., et al.
Pubblicazione: (2024)

Machine Learning-Enhanced Ant Colony Optimization for Column Generation
di: Xu, Hongjie, et al.
Pubblicazione: (2024)

Availability of Perfect Decomposition in Statistical Linkage Learning for Unitation-based Function Concatenations
di: Prusik, Michal, et al.
Pubblicazione: (2025)

Stable and Robust Deep Learning By Hyperbolic Tangent Exponential Linear Unit (TeLU)
di: Fernandez, Alfredo, et al.
Pubblicazione: (2024)

The Stacked Autoencoder Evolution Hypothesis
di: Iizuka, Hiroyuki
Pubblicazione: (2026)

Supervised Hebbian Learning
di: Alemanno, Francesco, et al.
Pubblicazione: (2022)

Physics-Informed Echo State Networks for Modeling Controllable Dynamical Systems
di: Mochiutti, Eric, et al.
Pubblicazione: (2024)

Prototype Analysis in Hopfield Networks with Hebbian Learning
di: McAlister, Hayden, et al.
Pubblicazione: (2024)

Curriculum Learning in Genetic Programming Guided Local Search for Large-scale Vehicle Routing Problems
di: Liu, Saining, et al.
Pubblicazione: (2025)

Design and Implementation of Hardware Accelerators for Neural Processing Applications
di: Mayannavar, Shilpa, et al.
Pubblicazione: (2024)

Compact Pixelated Microstrip Forward Broadside Coupler Using Binary Particle Swarm Optimization
di: Parsaei, Kourosh, et al.
Pubblicazione: (2024)

A Digital Pheromone-Based Approach for In-Control/Out-of-Control Classification
di: Pestana, Pedro, et al.
Pubblicazione: (2025)