Výsledky vyhledávání - "Learning and adaptive systems in artificial intelligence"

Artificial to Spiking Neural Networks Conversion with Calibration in Scientific Machine Learning: Artificial to spiking neural networks conversion with calibration in scientific machine learning

Autoři: Qian Zhang Chenxi Wu Adar Kahana a další

Zdroj: SIAM Journal on Scientific Computing. 47:C559-C577

Témata: scientific machine learning, PINN, Learning and adaptive systems in artificial intelligence, Artificial neural networks and deep learning, spiking neural network

Popis souboru: application/xml

Přístupová URL adresa: https://zbmath.org/8037156
https://doi.org/10.1137/24m1643232

CoCoGen: Physically Consistent and Conditioned Score-Based Generative Models for Forward and Inverse Problems: CoCoGen: physically consistent and conditioned score-based generative models for forward and inverse problems

Autoři: Christian Jacobsen Yilin Zhuang Karthik Duraisamy

Zdroj: SIAM Journal on Scientific Computing. 47:C399-C425

Témata: diffusion models, FOS: Computer and information sciences, Inverse problems for PDEs, Computer Science - Machine Learning, Partial differential equations of mathematical physics and other areas of application, generative AI, physical consistency, Learning and adaptive systems in artificial intelligence, computational physics, score-based generative models, conditional generative modeling, Machine Learning (cs.LG)

Popis souboru: application/xml

Přístupová URL adresa: http://arxiv.org/abs/2312.10527

Research on signal recognition method of acoustic beacon for underwater acoustic active and passive positioning dynamics system based on machine learning

Autoři: Xu, Jinjin

Zdroj: Journal of Combinatorial Mathematics and Combinatorial Computing. 124:159-171

Témata: Signal theory (characterization, reconstruction, filtering, etc.), machine learning, signal recognition, Learning and adaptive systems in artificial intelligence, underwater acoustic active and passive positioning system, acoustic beacon

Popis souboru: application/xml

DeepONet Based Preconditioning Strategies for Solving Parametric Linear Systems of Equations: DeepONet based preconditioning strategies for solving parametric linear systems of equations

Autoři: Alena Kopaničáková George Em Karniadakis

Zdroj: SIAM Journal on Scientific Computing. 47:C151-C181

Témata: Large-scale problems in mathematical programming, Iterative numerical methods for linear systems, operator learning, Multigrid methods, domain decomposition for initial value and initial-boundary value problems involving PDEs, preconditioning, Learning and adaptive systems in artificial intelligence, FOS: Mathematics, Krylov methods, Preconditioners for iterative methods, spectral bias, Mathematics - Numerical Analysis, Numerical Analysis (math.NA), hybridization

Popis souboru: application/xml

Přístupová URL adresa: http://arxiv.org/abs/2401.02016

Limits of Learning Dynamical Systems: Limits of learning dynamical systems

Autoři: Tyrus Berry Suddhasattwa Das

Zdroj: SIAM Review. 67:107-137

Témata: iterative forecast, Learning and adaptive systems in artificial intelligence, 37M99, 37N30, 37A20, 37D25, Dynamical Systems (math.DS), reservoir computing, direct forecast, Approximation methods and numerical treatment of dynamical systems, matrix cocycle, Dynamical systems in numerical analysis, delay coordinates, stochastic stability, FOS: Mathematics, mixing, Nonuniformly hyperbolic systems (Lyapunov exponents, Pesin theory, etc.), Mathematics - Dynamical Systems, Lyapunov exponent

Popis souboru: application/xml

Přístupová URL adresa: http://arxiv.org/abs/2409.13493

Reinforcement Learning‐Based Prescribed Performance Formation Control for USVs With Collision Avoidance and Preserved Connectivity: Reinforcement learning-based prescribed performance formation control for USVs with collision avoidance and preserved connectivity

Autoři: Gengqi Li Wei Wang Liang Cao

Zdroj: International Journal of Robust and Nonlinear Control. 35:2996-3012

Témata: reinforcement learning, optimal formation control, unmanned surface vehicles (USVs), Learning and adaptive systems in artificial intelligence, preserved connectivity, Automated systems (robots, etc.) in control theory, collision avoidance, monotone tube boundary function

Popis souboru: application/xml

Prescribed‐Time Optimal Formation Synchronous Tracking Control of UAV‐Ugvs Based on Reinforcement Learning: Prescribed-time optimal formation synchronous tracking control of UAV-UGVs based on reinforcement learning

Autoři: Shi‐Xun Xiong Guo‐Ping Jiang Yun‐Xia Zhu a další

Zdroj: International Journal of Robust and Nonlinear Control. 35:2437-2450

Témata: optimal control, reinforcement learning, Learning and adaptive systems in artificial intelligence, Automated systems (robots, etc.) in control theory, Existence theories for optimal control problems involving partial differential equations, UAV-UGV formation, prescribed-time

Popis souboru: application/xml

Přístupová URL adresa: https://zbmath.org/8034848
https://doi.org/10.1002/rnc.7808

Automobile Insurance Fraud Detection Based on PSO-XGBoost Model and Interpretable Machine Learning Method: Automobile insurance fraud detection based on PSO-XGBoost model and interpretable machine learning method

Autoři: Ding, Ning Ruan, Xiao Wang, Hao a další

Zdroj: Insurance: Mathematics and Economics. 120:51-60

Témata: fraud detection, automobile insurance, SHAP, Actuarial mathematics, Learning and adaptive systems in artificial intelligence, PSO-XGBoost

Popis souboru: application/xml

Přístupová URL adresa: https://zbmath.org/7984120
https://doi.org/10.1016/j.insmatheco.2024.11.006

Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-Informed Neural Networks Framework for Interface Problems: Adaptive interface-PINNs (AdaI-PINNs): an efficient physics-informed neural networks framework for interface problems

Autoři: Roy, Sumanta Annavarapu, Chandrasekhar Roy, Pratanu a další

Zdroj: Communications in Computational Physics. 37:603-622

Témata: FOS: Computer and information sciences, I-PINNs, Computer Science - Machine Learning, physics-informed machine learning, Learning and adaptive systems in artificial intelligence, interface problems, AdaI-PINNs, Computational methods for problems pertaining to mechanics of particles and systems, Machine Learning (cs.LG), domain decomposition, machine learning, Structured surfaces and interfaces, coexistent phases, PINN, Interface problems, diffusion-limited aggregation arising in equilibrium statistical mechanics, Partial differential equations and systems of partial differential equations with constant coefficients

Popis souboru: application/xml

Přístupová URL adresa: http://arxiv.org/abs/2406.04626

Learning large-scale industrial physics simulations

Autoři: Casenave, Fabien

Zdroj: European Mathematical Society Magazine. :5-13

Témata: Learning and adaptive systems in artificial intelligence, FOS: Mathematics, Mathematics - Numerical Analysis, Numerical Analysis (math.NA), General theory of mathematical modeling

Popis souboru: application/xml

Přístupová URL adresa: http://arxiv.org/abs/2502.08295

How Much Data Is Sufficient to Learn High-Performing Algorithms?: How much data is sufficient to learn high-performing algorithms?

Autoři: Maria-Florina Balcan Dan Deblasio Travis Dick a další

Zdroj: Journal of the ACM. 71:1-58

Témata: 0301 basic medicine, data-driven algorithm design, 0303 health sciences, 03 medical and health sciences, machine learning, computational biology, General topics in the theory of algorithms, Learning and adaptive systems in artificial intelligence, automated algorithm design, automated algorithm configuration

Popis souboru: application/xml

A Bayesian Approach for Learning Bayesian Network Structures: A Bayesian approach for learning Bayesian network structures

Autoři: Zareifard, Hamid Tabar, Vahid Rezaei Javidian, Mohammad Ali a další

Přispěvatelé: Zareifard, Hamid Tabar, Vahid Rezaei Javidian, Mohammad Ali a další

Zdroj: Lobachevskii Journal of Mathematics. 45:4434-4447

Témata: Bayesian network, directed acyclic graphs, structure learning, Estimation in multivariate analysis, Bayesian inference, Gibbs sampler, Learning and adaptive systems in artificial intelligence, Bayesian estimation, Monte Carlo, Probabilistic graphical models

Popis souboru: application/xml

Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution: Auto-weighted Bayesian physics-informed neural networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution

Autoři: Sarah Perez Philippe Poncet

Zdroj: Computational Geosciences. 28:1175-1215

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, Bayesian physics-informed neural networks, uncertainty quantification, Learning and adaptive systems in artificial intelligence, Computer Science - Neural and Evolutionary Computing, Numerical Analysis (math.NA), Inverse problems in geophysics, artificial intelligence, imaging inverse problem, 01 natural sciences, Machine Learning (cs.LG), pore-scale porous media, multi-objective training, 0103 physical sciences, FOS: Mathematics, Geostatistics, Hamiltonian Monte Carlo, Mathematics - Numerical Analysis, Neural and Evolutionary Computing (cs.NE), 0105 earth and related environmental sciences

Popis souboru: application/xml

Přístupová URL adresa: http://arxiv.org/abs/2308.12864

New Douglas-Rashford Splitting Algorithms for Generalized DC Programming with Applications in Machine Learning: New Douglas-Rashford splitting algorithms for generalized DC programming with applications in machine learning

Autoři: Yonghong Yao Lateef O. Jolaoso Yekini Shehu a další

Zdroj: Journal of Scientific Computing. 103

Témata: machine learning, Numerical mathematical programming methods, DC programming, Nonlinear programming, Optimization and Control (math.OC), nonconvex optimization, Learning and adaptive systems in artificial intelligence, FOS: Mathematics, Nonconvex programming, global optimization, Douglas-Rachford splitting algorithm, Mathematics - Optimization and Control

Popis souboru: application/xml

Přístupová URL adresa: http://arxiv.org/abs/2404.14800

Self‐triggered control for linear systems based on hierarchical reinforcement learning: Self-triggered control for linear systems based on hierarchical reinforcement learning

Autoři: Wei Chen Haiying Wan Xiaoli Luan a další

Zdroj: International Journal of Robust and Nonlinear Control. 34:9112-9129

Témata: Hierarchical systems, 0209 industrial biotechnology, self-triggered control, Linear systems in control theory, model-free, Learning and adaptive systems in artificial intelligence, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, hierarchical reinforcement learning

Popis souboru: application/xml

A multi-aggregator graph neural network for backbone exaction of fracture networks

Autoři: Zheng, Tianji Sun, Chengcheng Zhang, Jian a další

Zdroj: Computational Geosciences. 28:537-549

Témata: inductive learning, Computational methods for problems pertaining to geophysics, backbone extraction, Learning and adaptive systems in artificial intelligence, graph neural network, discrete fracture networks

Popis souboru: application/xml

Impact of thermal radiations and viscous dissipation during the boundary layer non‐Newtonian fluid flow past a slender needle: Probed by machine learning: Impact of thermal radiations and viscous dissipation during the boundary layer non-Newtonian fluid flow past a slender needle: probed by machine learning

Autoři: Hongxing Yao Asad Ullah null Ikramullah a další

Zdroj: ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik. 105

Témata: similarity transform, Non-Newtonian fluids, Learning and adaptive systems in artificial intelligence, Levenberg-Marquardt algorithm: parametric study, Joule heating, Magnetohydrodynamics and electrohydrodynamics, Basic methods in fluid mechanics, Diffusive and convective heat and mass transfer, heat flow, magnetic field, artificial neural network, Artificial neural networks and deep learning, Casson fluid

Popis souboru: application/xml

Random features and polynomial rules

Autoři: Fabian Aguirre-Lopez Silvio Franz Mauro Pastore

Zdroj: SciPost Physics, Vol 18, Iss 1, p 039 (2025)

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, Physics, QC1-999, Learning and adaptive systems in artificial intelligence, FOS: Physical sciences, Disordered Systems and Neural Networks (cond-mat.dis-nn), Condensed Matter - Disordered Systems and Neural Networks, Disordered systems (random Ising models, random Schrödinger operators, etc.) in equilibrium statistical mechanics, Artificial neural networks and deep learning, Machine Learning (cs.LG)

Popis souboru: application/xml; application/pdf

Přístupová URL adresa: http://arxiv.org/abs/2402.10164
https://zbmath.org/7982799
https://doi.org/10.21468/scipostphys.18.1.039
https://doaj.org/article/04cae1a59a9b49168a087650d8e904b0
https://scipost.org/10.21468/SciPostPhys.18.1.039
https://doi.org/10.21468/SciPostPhys.18.1.039
https://hdl.handle.net/11587/552001

Data Augmentation for the POD Formulation of the Parametric Laminar Incompressible Navier–Stokes Equations: Data augmentation for the formulation of the parametric laminar incompressible Navier-Stokes equations

Autoři: Muixí Ballonga, Alba Zlotnik, Sergio Giacomini, Matteo a další

Zdroj: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

Témata: Scientific machine learning, FOS: Computer and information sciences, Reduced-order models, Data augmentation, Incompressible Navier–Stokes, reduced-order model, Navier-Stokes equations for incompressible viscous fluids, Learning and adaptive systems in artificial intelligence, Fluid Dynamics (physics.flu-dyn), machine learning: jet flow control, FOS: Physical sciences, Physics - Fluid Dynamics, Numerical Analysis (math.NA), Proper orthogonal decomposition, Newton-Raphson method, Computational Engineering, Finance, and Science (cs.CE), proper orthogonal decomposition, lid-driven cavity, 65M60, 76D05, 76D55, FOS: Mathematics, Mathematics - Numerical Analysis, Computer Science - Computational Engineering, Finance, and Science, Finite element methods applied to problems in fluid mechanics

Popis souboru: application/pdf; application/xml

Přístupová URL adresa: http://arxiv.org/abs/2312.14756
https://zbmath.org/7982176
https://doi.org/10.1002/nme.7624
https://hdl.handle.net/2117/425940
https://doi.org/10.1002/nme.7624

A LoRA-Based Approach to Fine-Tuning LLMs for Educational Guidance in Resource-Constrained Settings

Autoři: Hosen, Md Millat

Témata: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, 68T05 (Learning and adaptive systems), 68T07 (Artificial intelligence and education)

Přístupová URL adresa: http://arxiv.org/abs/2504.15610