Cover Image

Systematic construction of continuous-time neural networks for linear dynamical systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Datar, Chinmay, Datar, Adwait, Dietrich, Felix, Schilders, Wil
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Numerical Analysis Dynamical Systems 93B17, 65L70, 68T07 I.2.m; G.1.3; G.1.7
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning Hamiltonian flows from numerical integrators and examples
by: Fang, Rui, et al.
Published: (2025)

Latent Twins
by: Chung, Matthias, et al.
Published: (2025)

On the study of frequency control and spectral bias in Wavelet-Based Kolmogorov Arnold networks: A path to physics-informed KANs
by: Meshir, Juan Daniel, et al.
Published: (2025)

A Structure-Preserving PML-Domain-Embedding Method for Acoustic Wave Scattering by Moving Objects
by: Gu, Xuelong, et al.
Published: (2026)

Learning Chaotic Dynamics through Second-Order Geometric Supervision
by: Kang, Shinhoo, et al.
Published: (2026)

The structural method for Ordinary Differential Equations
by: Clain, S., et al.
Published: (2025)

IGA-ODIL: Optimizing DIscretre robust Loss with Isogeometric Analysis to solve forward and inverse problems faster using machine learning tools
by: Paszyński, Maciej, et al.
Published: (2026)

A Least-Squares-Based Neural Network (LS-Net) for Solving Linear Parametric PDEs
by: Baharlouei, Shima, et al.
Published: (2024)

Fully-Automated Verification of Linear Systems Using Inner- and Outer-Approximations of Reachable Sets
by: Wetzlinger, Mark, et al.
Published: (2022)

Challenges in automatic differentiation and numerical integration in physics-informed neural networks modelling
by: Daněk, Josef, et al.
Published: (2024)

Python library supporting Discrete Variational Formulations and training solutions with Collocation-based Robust Variational Physics Informed Neural Networks (DVF-CRVPINN)
by: Służalec, Tomasz, et al.
Published: (2026)

Collocation-based Robust Variational Physics-Informed Neural Networks (CRVPINN)
by: Łoś, Marcin, et al.
Published: (2024)

A Classical-Quantum Hybrid Architecture for Physics-Informed Neural Networks
by: Lantigua, Said, et al.
Published: (2025)

A multiphase cubic MARS method for fourth- and higher-order interface tracking of two or more materials with arbitrary topology and geometry
by: Tan, Yan, et al.
Published: (2025)

FSampler: Training Free Acceleration of Diffusion Sampling via Epsilon Extrapolation
by: Vladimir, Michael A.
Published: (2025)

Leveraging Mixed Precision in Exponential Time Integration Methods
by: Balos, Cody J., et al.
Published: (2023)

Sub-Riemannian Landmark Matching and its interpretation as residual neural networks
by: Jansson, Erik, et al.
Published: (2022)

When can a neural operator replace a coarse solve? Architectural principles for two-level preconditioning
by: Melchers, Hugo, et al.
Published: (2026)

Learning solutions of parameterized stiff ODEs using Gaussian processes
by: Garcia, Idoia Cortes, et al.
Published: (2025)

When can forward stable algorithms be composed stably?
by: Beltrán, Carlos, et al.
Published: (2021)

Gelation in input-driven aggregation
by: Krapivsky, P. L., et al.
Published: (2024)

Alternately-optimized SNN method for acoustic scattering problem in unbounded domain
by: Song, Haoming, et al.
Published: (2025)

Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids
by: Cho, Sung Woong, et al.
Published: (2024)

Numerical methods for Chaotic ODE
by: Corless, Robert M.
Published: (2025)

An accelerated frequency-independent solver for oscillatory differential equations
by: Stojimirovic, Tara, et al.
Published: (2024)

High-order Gauss-Legendre methods admit a composition representation and a conjugate-symplectic counterpart
by: Iavernaro, Felice, et al.
Published: (2025)

Mixed Precision Training of Neural ODEs
by: Celledoni, Elena, et al.
Published: (2025)

Second-order exponential splittings in the presence of unbounded and time-dependent operators: construction and convergence
by: Kropielnicka, Karolina, et al.
Published: (2023)

Solving Nonlinear Energy Supply and Demand System Using Physics-Informed Neural Networks
by: Vo, Van Truong, et al.
Published: (2024)

Dual-Solver: A Generalized ODE Solver for Diffusion Models with Dual Prediction
by: Park, Soochul, et al.
Published: (2026)

KAN-ODEs: Kolmogorov-Arnold Network Ordinary Differential Equations for Learning Dynamical Systems and Hidden Physics
by: Koenig, Benjamin C., et al.
Published: (2024)

Mathematical Modeling of Option Pricing with an Extended Black-Scholes Framework
by: Nayak, Nikhil Shivakumar
Published: (2025)

Velocity-Inferred Hamiltonian Neural Networks: Learning Energy-Conserving Dynamics from Position-Only Data
by: Xu, Ruichen, et al.
Published: (2025)

Pyroclast: A Modular High-Performance Python Solver for Geodynamics
by: Ferrari, Marcel
Published: (2026)

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model
by: Cho, Hyunwoo, et al.
Published: (2024)

Graph grammars and Physics Informed Neural Networks for simulating of pollution propagation on Spitzbergen
by: Sikora, Maciej, et al.
Published: (2024)

The Evolution of Computer-Assisted Proof In Analysis
by: Rychlik, Marek
Published: (2026)

Conjugate Gradient Methods are Not Efficient: Experimental Study of the Locality Limitation
by: Rüde, Ulrich
Published: (2026)

A-FloPS: Accelerating Diffusion Models via Adaptive Flow Path Sampler
by: Jin, Cheng, et al.
Published: (2025)

Collocation-based Robust Physics Informed Neural Networks for time-dependent simulations of pollution propagation under thermal inversion conditions on Spitsbergen
by: Siwik, Leszek, et al.
Published: (2026)