Results 91 to 100 of about 35,710 (245)

Incremental Model Order Reduction of Smoothed‐Particle Hydrodynamic Simulations

International Journal for Numerical Methods in Fluids, EarlyView.
The paper presents the development of an incremental singular value decomposition strategy for compressing time‐dependent particle simulation results, addressing gaps in the data matrices caused by temporally inactive particles. The approach reduces memory requirements by about 90%, increases the computational effort by about 10%, and preserves the ...
Eduardo Di Costanzo, Niklas Kühl, Jean‐Christophe Marongiu, Thomas Rung   +3 more
wiley   +1 more source

Numerical differentiation of fractional order derivatives based on inverse source problems of hyperbolic equations

Mathematical Modelling and Analysis
In this paper, we mainly study the numerical differentiation problem of computing the fractional order derivatives from noise data of a single variable function.
Zewen Wang, Shufang Qiu, Xiuxing Rui, Wen Zhang   +3 more
doaj   +1 more source

Aging of Tetrafluoroethylene Propylene (FEPM) Material in High‐Pressure HighTemperature Hydrogen Sulfide Downhole Environment: Theory, Modeling, Experiments, and Material Lifetime Prediction

Macromolecular Theory and Simulations, EarlyView.
A novel approach that integrates AI‐based methodology, laboratory experimentation, and field application experience to accurately predict the lifespan of elastomeric materials in harsh downhole environments. ABSTRACT This paper presents a physics‐driven neural network (PINN) model to capture the constitutive behavior, elongation at break (EAB), modulus,
Aref Ghaderi, Hadis Nouri, Roozbeh Dargazany, Scott Meng, Peixiang Xing, Jiaxiang Ren, Peng Cheng   +6 more
wiley   +1 more source

Unveiling New Perspectives on the Hirota–Maccari System With Multiplicative White Noise

Mathematical Methods in the Applied Sciences, EarlyView.
ABSTRACT In this study, we delve into the stochastic Hirota–Maccari system, which is subjected to multiplicative noise according to the Itô sense. The stochastic Hirota–Maccari system is significant for its ability to accurately model how stochastic affects nonlinear wave propagation, providing valuable insights into complex systems like fluid dynamics
Mohamed E. M. Alngar, Khaled A. Gepreel, Reham M. A. Shohib, Yakup Yildirim   +3 more
wiley   +1 more source

Sample complexity for inverse problems in PDE [PDF]

, 2022

openalex   +1 more source

Sparse Reconstructions for Inverse PDE Problems

, 2009
We are concerned with the numerical solution of linear parameter identification problems for parabolic PDE, written as an operator equation $Ku=f$. The target object $u$ is assumed to have a sparse expansion with respect to a wavelet system $Psi={psi_lambda}$ in space-time, being equivalent to a priori information on the regularity of $u=mathbf u ...
openaire   +3 more sources

A Variational Formulation of European Option Prices in the 1‐Hypergeometric Stochastic Volatility Model

Mathematical Methods in the Applied Sciences, EarlyView.
ABSTRACT The paper proposes a variational analysis of the 1‐hypergeometric stochastic volatility model for pricing European options. The methodology involves the derivation of estimates of the weak solution in a weighted Sobolev space. The weight is closely related to the stochastic volatility dynamic of the model.
José Da Fonseca, Wenjun Zhang
wiley   +1 more source

Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems Using Transfer Learning

Applied Sciences
Physics-Informed Neural Network (PINN) is a data-driven solver for partial and ordinary differential equations (ODEs/PDEs). It provides a unified framework to address both forward and inverse problems.
Abdul Hannan Mustajab, Hao Lyu, Zarghaam Rizvi, Frank Wuttke   +3 more
doaj   +1 more source

Ellipsoid‐Based Interval‐Type Uncertainty Model Updating Based on Riemannian Manifold and Gaussian Process Model

International Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT Modern engineering systems require advanced uncertainty‐aware model updating methods that address parameter correlations beyond conventional interval analysis. This paper proposes a novel framework integrating Riemannian manifold theory with Gaussian Process Regression (GPR) for systems governed by Symmetric Positive‐Definite (SPD) matrix ...
Yanhe Tao, Qintao Guo, Jin Zhou, Cheng Yi   +3 more
wiley   +1 more source

M-WDRNNs: Mixed-Weighted Deep Residual Neural Networks for Forward and Inverse PDE Problems [PDF]

, 2023
Jiachun Zheng, Yunlei Yang
openalex   +1 more source