Search Results - "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

Authors: Qian Zhang Chenxi Wu Adar Kahana et al.

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

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

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Access URL: 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

Authors: Christian Jacobsen Yilin Zhuang Karthik Duraisamy

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

Subject Terms: 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)

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Access URL: 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

Authors: Xu, Jinjin

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

Subject Terms: 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

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

Authors: Alena Kopaničáková George Em Karniadakis

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

Subject Terms: 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

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Access URL: http://arxiv.org/abs/2401.02016

Limits of Learning Dynamical Systems: Limits of learning dynamical systems

Authors: Tyrus Berry Suddhasattwa Das

Source: SIAM Review. 67:107-137

Subject Terms: 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

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Access URL: 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

Authors: Gengqi Li Wei Wang Liang Cao

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

Subject Terms: 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

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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

Authors: Shi‐Xun Xiong Guo‐Ping Jiang Yun‐Xia Zhu et al.

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

Subject Terms: 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

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Access URL: 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

Authors: Ding, Ning Ruan, Xiao Wang, Hao et al.

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

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

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Access URL: 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

Authors: Roy, Sumanta Annavarapu, Chandrasekhar Roy, Pratanu et al.

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

Subject Terms: 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

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Access URL: http://arxiv.org/abs/2406.04626

Learning large-scale industrial physics simulations

Authors: Casenave, Fabien

Source: European Mathematical Society Magazine. :5-13

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

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Access URL: 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?

Authors: Maria-Florina Balcan Dan Deblasio Travis Dick et al.

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

Subject Terms: 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

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

Authors: Zareifard, Hamid Tabar, Vahid Rezaei Javidian, Mohammad Ali et al.

Contributors: Zareifard, Hamid Tabar, Vahid Rezaei Javidian, Mohammad Ali et al.

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

Subject Terms: 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

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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

Authors: Sarah Perez Philippe Poncet

Source: Computational Geosciences. 28:1175-1215

Subject Terms: 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

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Access URL: 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

Authors: Yonghong Yao Lateef O. Jolaoso Yekini Shehu et al.

Source: Journal of Scientific Computing. 103

Subject Terms: 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

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Access URL: 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

Authors: Wei Chen Haiying Wan Xiaoli Luan et al.

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

Subject Terms: 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

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

Authors: Zheng, Tianji Sun, Chengcheng Zhang, Jian et al.

Source: Computational Geosciences. 28:537-549

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

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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

Authors: Hongxing Yao Asad Ullah null Ikramullah et al.

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

Subject Terms: 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

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Random features and polynomial rules

Authors: Fabian Aguirre-Lopez Silvio Franz Mauro Pastore

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

Subject Terms: 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)

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Access URL: 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

Authors: Muixí Ballonga, Alba Zlotnik, Sergio Giacomini, Matteo et al.

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

Subject Terms: 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

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Access URL: 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

Authors: Hosen, Md Millat

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

Access URL: http://arxiv.org/abs/2504.15610