Copertina

Constrained Hamiltonian Systems and Physics-Informed Neural Networks: Hamilton-Dirac Neural Networks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Kaltsas, Dimitrios A.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Hamiltonian Learning via Inverse Physics-Informed Neural Networks
di: Liu, Jie, et al.
Pubblicazione: (2025)

Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
di: Hoshisashi, Kentaro, et al.
Pubblicazione: (2026)

Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?
di: Kim, Tae-Geun, et al.
Pubblicazione: (2024)

Physics-Informed Neural Networks in Electromagnetic and Nanophotonic Design
di: Abdelraouf, Omar A. M., et al.
Pubblicazione: (2025)

Quantum Orthogonal Separable Physics-Informed Neural Networks
di: Zanotta, Pietro, et al.
Pubblicazione: (2025)

Statistical Learning Analysis of Physics-Informed Neural Networks
di: Barajas-Solano, David A.
Pubblicazione: (2026)

Evidential Physics-Informed Neural Networks
di: Tan, Hai Siong, et al.
Pubblicazione: (2025)

Physics-Informed Neural Networks with Dynamical Boundary Constraints
di: Martínez-Esteban, Andrés, et al.
Pubblicazione: (2025)

Quantum Noise Tomography with Physics-Informed Neural Networks
di: Sulc, Antonin
Pubblicazione: (2025)

Boundary-Informed Method of Lines for Physics Informed Neural Networks
di: Cederholm, Maximilian, et al.
Pubblicazione: (2025)

Physics-Informed Neural Network for Elastic Wave-Mode Separation
di: Alves, E. A. B., et al.
Pubblicazione: (2025)

Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks
di: Haywood-Alexander, Marcus, et al.
Pubblicazione: (2024)

DiffGrad for Physics-Informed Neural Networks
di: Rahman, Jamshaid Ul, et al.
Pubblicazione: (2024)

Bayesian Reasoning for Physics Informed Neural Networks
di: Graczyk, Krzysztof M., et al.
Pubblicazione: (2023)

NeuroPINNs: Neuroscience Inspired Physics Informed Neural Networks
di: Garg, Shailesh, et al.
Pubblicazione: (2025)

Physics Informed Neural Network Framework for Unsteady Discretized Reduced Order System
di: Halder, Rahul, et al.
Pubblicazione: (2023)

Physics Informed Neural Networks for Free Shear Flows
di: Raghu, Siddharth, et al.
Pubblicazione: (2024)

Evidential Physics-Informed Neural Networks for Scientific Discovery
di: Tan, Hai Siong, et al.
Pubblicazione: (2025)

Symmetry-Constrained Multi-Scale Physics-Informed Neural Networks for Graphene Electronic Band Structure Prediction
di: Lee, Wei Shan, et al.
Pubblicazione: (2025)

Physics-Informed Neural Networks for Maximizing Quantum Fisher Information in Time-Dependent Many-Body Systems
di: Ferrer-Sánchez, Antonio, et al.
Pubblicazione: (2026)

On the Role of Consistency Between Physics and Data in Physics-Informed Neural Networks
di: Becerra-Zuniga, Nicolás, et al.
Pubblicazione: (2026)

KP-PINNs: Kernel Packet Accelerated Physics Informed Neural Networks
di: Yang, Siyuan, et al.
Pubblicazione: (2025)

Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks
di: Dangel, Felix, et al.
Pubblicazione: (2024)

PINGS: Physics-Informed Neural Network for Fast Generative Sampling
di: Prasha, Achmad Ardani, et al.
Pubblicazione: (2025)

Physics-Informed Neural Networks for Parametric Compressible Euler Equations
di: Wassing, Simon, et al.
Pubblicazione: (2023)

A Physics-Informed Neural Network with a Modified Lorentzian Activation for Nonlocal Gradient-Flow Equations in Dynamic Density Functional Theory
di: Gourzoulidis, Dimitrios, et al.
Pubblicazione: (2026)

PINNfluence: Influence Functions for Physics-Informed Neural Networks
di: Naujoks, Jonas R., et al.
Pubblicazione: (2024)

Exact Enforcement of Temporal Continuity in Sequential Physics-Informed Neural Networks
di: Roy, Pratanu, et al.
Pubblicazione: (2024)

Inverse Physics-Informed Neural Networks for transport models in porous materials
di: Berardi, Marco, et al.
Pubblicazione: (2024)

Physics-Informed Neural Networks with Fourier Features and Attention-Driven Decoding
di: Arni, Rohan, et al.
Pubblicazione: (2025)

Reconstruction of Excitation Waves from Mechanical Deformation using Physics-Informed Neural Networks
di: Dermul, Nathan, et al.
Pubblicazione: (2024)

Physics-Informed Neural Networks for Irregular Domain Mapping and Partial Differential Equations solving
di: Zhou, Cuizhi, et al.
Pubblicazione: (2025)

Reconstructing Relativistic Magnetohydrodynamics with Physics-Informed Neural Networks
di: Cheung, Corwin, et al.
Pubblicazione: (2025)

Physics-Informed Neural Networks for microflows: Rarefied Gas Dynamics in Cylinder Arrays
di: Tucny, Jean-Michel, et al.
Pubblicazione: (2025)

From Fermat's Principle to Physics-Informed Neural Networks: A Unified Computational Approach to Variational Physics
di: Razdan, Aman, et al.
Pubblicazione: (2026)

Leveraging Influence Functions for Resampling Data in Physics-Informed Neural Networks
di: Naujoks, Jonas R., et al.
Pubblicazione: (2025)

Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
di: Jekic, Aleksandra, et al.
Pubblicazione: (2025)

Physics-Constrained Graph Neural Networks for Spatio-Temporal Prediction of Drop Impact on OLED Display Panels
di: Kim, Jiyong, et al.
Pubblicazione: (2024)

Kolmogorov-Arnold Representation for Symplectic Learning: Advancing Hamiltonian Neural Networks
di: Wu, Zongyu, et al.
Pubblicazione: (2025)

Residual Attention Physics-Informed Neural Networks for Robust Multiphysics Simulation of Steady-State Electrothermal Energy Systems
di: Zhou, Yuqing, et al.
Pubblicazione: (2026)