Copertina

Parsimonious Physics-Informed Random Projection Neural Networks for Initial-Value Problems of ODEs and index-1 DAEs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Fabiani, Gianluca, Galaris, Evangelos, Russo, Lucia, Siettos, Constantinos
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Numerical Analysis Machine Learning Dynamical Systems
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Linear Stability Analysis of Physics-Informed Random Projection Neural Networks for ODEs
di: Fabiani, Gianluca, et al.
Pubblicazione: (2024)

Slow Invariant Manifolds of Singularly Perturbed Systems via Physics-Informed Machine Learning
di: Patsatzis, Dimitrios G., et al.
Pubblicazione: (2023)

Nonlinear Discrete-Time Observers with Physics-Informed Neural Networks
di: Alvarez, Hector Vargas, et al.
Pubblicazione: (2024)

A physics-informed neural network method for the approximation of slow invariant manifolds for the general class of stiff systems of ODEs
di: Patsatzis, Dimitrios G., et al.
Pubblicazione: (2024)

Stability and Bifurcation Analysis of Nonlinear PDEs via Random Projection-based PINNs: A Krylov-Arnoldi Approach
di: Fabiani, Gianluca, et al.
Pubblicazione: (2026)

Learning the Latent dynamics of Fluid flows from High-Fidelity Numerical Simulations using Parsimonious Diffusion Maps
di: Della Pia, Alessandro, et al.
Pubblicazione: (2024)

GoRINNs: Godunov-Riemann Informed Neural Networks for Learning Hyperbolic Conservation Laws
di: Patsatzis, Dimitrios G., et al.
Pubblicazione: (2024)

RandONet: Shallow-Networks with Random Projections for learning linear and nonlinear operators
di: Fabiani, Gianluca, et al.
Pubblicazione: (2024)

PDE-Free Mass-Constrained Learning of Complex Systems with Hidden States
di: Viola, Gianmaria, et al.
Pubblicazione: (2025)

Surrogate normal-forms for the numerical bifurcation and stability analysis of navier-stokes flows via machine learning
di: Della Pia, Alessandro, et al.
Pubblicazione: (2025)

Semi-Explicit Neural DAEs: Learning Long-Horizon Dynamical Systems with Algebraic Constraints
di: Pal, Avik, et al.
Pubblicazione: (2025)

Data-driven Control of Agent-based Models: an Equation/Variable-free Machine Learning Approach
di: Patsatzis, Dimitrios G., et al.
Pubblicazione: (2022)

Fredholm Neural Networks
di: Georgiou, Kyriakos, et al.
Pubblicazione: (2024)

HEATNETs: Explainable Random Feature Neural Networks for High-Dimensional Parabolic PDEs
di: Georgiou, Kyriakos, et al.
Pubblicazione: (2025)

Incorporating Continuous Dependence Qualifies Physics-Informed Neural Networks for Operator Learning
di: Li, Guojie, et al.
Pubblicazione: (2026)

Going with the Flow: Solving for Symmetry-Driven PDE dynamics with Physics-informed Neural Networks
di: Kavousanakis, Michail, et al.
Pubblicazione: (2025)

Equation-Free Coarse Control of Distributed Parameter Systems via Local Neural Operators
di: Fabiani, Gianluca, et al.
Pubblicazione: (2025)

Invariant Manifolds of Discrete-time Dynamical Systems with Nonlinear Exosystems via Hybrid Physics-Informed Neural Networks
di: Patsatzis, Dimitrios G., et al.
Pubblicazione: (2025)

Convergence Analysis of the Random Ordinate Method for Mitigating the Ray Effect
di: Li, Lei, et al.
Pubblicazione: (2025)

Randomized time stepping of nonlinearly parametrized solutions of evolution problems
di: Dong, Yijun, et al.
Pubblicazione: (2025)

Torus Time-Spectral Method for Quasi-Periodic Problems
di: He, Sicheng, et al.
Pubblicazione: (2025)

Modelling parametric uncertainty in PDEs models via Physics-Informed Neural Networks
di: Panahi, Milad, et al.
Pubblicazione: (2024)

CP-PINNs: Data-Driven Changepoints Detection in PDEs Using Online Optimized Physics-Informed Neural Networks
di: Dong, Zhikang, et al.
Pubblicazione: (2022)

Discontinuity Computing using Physics-Informed Neural Network
di: Liu, Li, et al.
Pubblicazione: (2022)

Generalized Lagrangian Neural Networks
di: Xiao, Shanshan, et al.
Pubblicazione: (2024)

Direct Finite-Time Contraction (Step-Log) Profiling--Driven Optimization of Parallel Schemes for Nonlinear Problems on Multicore Architectures
di: Shams, Mudassir, et al.
Pubblicazione: (2026)

Improving PINNs By Algebraic Inclusion of Boundary and Initial Conditions
di: Ren, Mohan, et al.
Pubblicazione: (2024)

State Estimation Using Sparse DEIM and Recurrent Neural Networks
di: Farazmand, Mohammad
Pubblicazione: (2024)

Adjoint-Based Projections for Uncertainty Quantification near Stochastically Perturbed Limit Cycles and Tori
di: Ahsan, Zaid, et al.
Pubblicazione: (2024)

A Full-Density Approach to Simulating Random Iteration Equations with Applications
di: Hoegele, Wolfgang
Pubblicazione: (2026)

Enhancing Arterial Blood Flow Simulations through Physics-Informed Neural Networks
di: Bhargava, Shivam, et al.
Pubblicazione: (2024)

Physics-Informed Functional Link Constrained Framework with Domain Mapping for Solving Bending Analysis of an Exponentially Loaded Perforated Beam
di: Sahu, Iswari, et al.
Pubblicazione: (2026)

Data-driven modelling of brain activity using neural networks, Diffusion Maps, and the Koopman operator
di: Gallos, Ioannis K., et al.
Pubblicazione: (2023)

SPIKE: Sparse Koopman Regularization for Physics-Informed Neural Networks
di: Miñoza, Jose Marie Antonio
Pubblicazione: (2026)

Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks
di: Hu, Zheyuan, et al.
Pubblicazione: (2023)

Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations
di: Hu, Zheyuan, et al.
Pubblicazione: (2024)

Physics-Informed Boundary Integral Networks (PIBI-Nets): A Data-Driven Approach for Solving Partial Differential Equations
di: Nagy-Huber, Monika, et al.
Pubblicazione: (2023)

A Meshless Solver for Blood Flow Simulations in Elastic Vessels Using Physics-Informed Neural Network
di: Zhang, Han, et al.
Pubblicazione: (2023)

A Frobenius-Optimal Projection for Enforcing Linear Conservation in Learned Dynamical Models
di: Mango, John M., et al.
Pubblicazione: (2025)

On the Unique Recovery of Transport Maps and Vector Fields from Finite Measure-Valued Data
di: Botvinick-Greenhouse, Jonah, et al.
Pubblicazione: (2026)