Imagen de Portada

USA Tariffs Effect: Machine Learning Insights into the Stock Market

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Patel, Mridul
Formato:	Preprint
Publicado:	2025
Materias:	Numerical Analysis
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

A Machine Learning Model for Solving Lane-Emden Equation using Legendre Wavelet Neural Network
por: Patel, Vijay Kumar, et al.
Publicado: (2024)

Machine Learning for highly oscillatory differential equations
por: Bouchereau, Maxime
Publicado: (2026)

Beyond Loss Guidance: Using PDE Residuals as Spectral Attention in Diffusion Neural Operators
por: Sawhney, Medha, et al.
Publicado: (2025)

Physics-based Machine Learning for Computational Fracture Mechanics
por: Aldakheel, Fadi, et al.
Publicado: (2025)

PCELM: Perturbation-Correction Extreme Learning Machine for the Stefan problem
por: Liu, Wenjie, et al.
Publicado: (2026)

Using the Newton-Raphson Method with Automatic Differentiation to Numerically Solve Implied Volatility of Stock Option through Binomial Model
por: Wunkaew, Wanchaloem, et al.
Publicado: (2022)

Reactive Transport Modeling with Physics-Informed Machine Learning for Critical Minerals Applications
por: Adhikari, K., et al.
Publicado: (2025)

Generalization Error Estimates of Machine Learning Methods for Solving High Dimensional Schrödinger Eigenvalue Problems
por: Yu, Hao, et al.
Publicado: (2024)

A Machine Learning and Finite Element Framework for Inverse Elliptic PDEs via Dirichlet-to-Neumann Mapping
por: Park, Dabin, et al.
Publicado: (2025)

Operator Learning at Machine Precision
por: Bacho, Aras, et al.
Publicado: (2025)

Eig-PIELM: A Mesh-Free Approach for Efficient Eigen-Analysis with Physics-Informed Extreme Learning Machines
por: Mishra, Rishi, et al.
Publicado: (2025)

Quantifying Correlations of Machine Learning Models
por: Li, Yuanyuan, et al.
Publicado: (2025)

On the Cyclostationary Linear Inverse Models: A Mathematical Insight and Implication
por: Lien, Justin, et al.
Publicado: (2024)

Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches
por: Wang, Yixuan
Publicado: (2026)

Solving PDEs on Unknown Manifolds with Machine Learning
por: Liang, Senwei, et al.
Publicado: (2021)

Isogeometric Simulation and Shape Optimization with Applications to Electrical Machines
por: Gangl, Peter, et al.
Publicado: (2018)

On the Limits of Interpretable Machine Learning in Quintic Root Classification
por: Thomas, Rohan, et al.
Publicado: (2026)

Machine learning-based parameter optimization for Müntz spectral methods
por: Zeng, Wei, et al.
Publicado: (2025)

Turing meets Moore-Penrose: Computing the Pseudoinverse on Turing Machines
por: Boche, Holger, et al.
Publicado: (2022)

Machine Learning-Based Nonlinear Nudging for Chaotic Dynamical Systems
por: Oh, Jaemin, et al.
Publicado: (2025)

Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
por: Chen, Jiale, et al.
Publicado: (2024)

From Obstacle Problems to Neural Insights: Feed Forward Neural Network Modeling of Ice Thickness
por: Chawla, Kapil, et al.
Publicado: (2024)

Computational Advantages of Multi-Grade Deep Learning: Convergence Analysis and Performance Insights
por: Fang, Ronglong, et al.
Publicado: (2025)

Quantum Error Detection For Early Term Fault-Tolerant Quantum Algorithms
por: Ginsberg, Tom, et al.
Publicado: (2025)

Computational Control of Nonlinear Partial Differential Equations Using Machine Learning
por: Kurbanov, Maximilian, et al.
Publicado: (2026)

Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning
por: Dereziński, Michał, et al.
Publicado: (2024)

Computational Insights into Orthotropic Fracture: Crack-Tip Fields in Strain-Limiting Materials under Non-Uniform Loads
por: Ghosh, Saugata, et al.
Publicado: (2025)

Variational (Energy-Based) Spectral Learning: A Machine Learning Framework for Solving Partial Differential Equations
por: Hammad, M. M.
Publicado: (2026)

Weak Form Scientific Machine Learning: Test Function Construction for System Identification
por: Tran, April, et al.
Publicado: (2025)

Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior
por: Subramanian, Shashank, et al.
Publicado: (2023)

Parametric Intrusive Reduced Order Models enhanced with Machine Learning Correction Terms
por: Ivagnes, Anna, et al.
Publicado: (2024)

An Orthogonal Polynomial Kernel-Based Machine Learning Model for Differential-Algebraic Equations
por: Taheri, Tayebeh, et al.
Publicado: (2024)

Scientific Applications Leveraging Randomized Linear Algebra
por: Patel, Vivak, et al.
Publicado: (2025)

An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
por: Gonon, Lukas, et al.
Publicado: (2024)

Machine Learning Algorithms to Assess Site Closure Time Frames for Soil and Groundwater Contamination
por: Le, Vu-Anh, et al.
Publicado: (2024)

Escaping Spectral Bias without Backpropagation: Fast Implicit Neural Representations with Extreme Learning Machines
por: Cho, Woojin, et al.
Publicado: (2026)

A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations
por: Aghaei, Alireza Afzal
Publicado: (2024)

Effect of temperature-dependent material properties on thermal regulation in microvascular composites
por: Adhikari, K., et al.
Publicado: (2024)

Performance of Machine Learning Methods for Gravity Inversion: Successes and Challenges
por: Negahdari, Vahid, et al.
Publicado: (2025)

Exact Constraint Enforcement in Physics-Informed Extreme Learning Machines using Null-Space Projection Framework
por: Mishra, Rishi, et al.
Publicado: (2026)