Results 111 to 120 of about 35,710 (245)

Macroscopic Market Making Games

Mathematical Finance, EarlyView.
ABSTRACT Building on the macroscopic market making framework as a control problem, this paper investigates its extension to stochastic games. In the context of price competition, each agent is benchmarked against the best quote offered by the others. We begin with the linear case.
Ivan Guo, Shijia Jin
wiley   +1 more source

One-Dimensional Elastic and Viscoelastic Full-Waveform Inversion in Heterogeneous Media Using Physics-Informed Neural Networks

IEEE Access
In this study, we discuss a mathematical framework to handle the inverse problem for the applications of partial differential equations (PDEs). In particular, we focus on wave equations and attempt to identify the wave parameters such as wave velocity ...
Alireza Pakravan
doaj   +1 more source

Theoretical and numerical results for some inverse problems for PDEs

, 2021
We consider geometric inverse problems concerning the one-dimensional Burgers equation and some related nonlinear systems (involving heat effects and variable density). In these problems, the goal is to find the size of the spatial interval from some appropriate boundary observations of the solution.
Apraiz, Jone, Doubova, Anna, Fernández Cara, Enrique, Yamamoto, Masahiro   +3 more
openaire   +1 more source

Multimodal Mamba with multitask learning for building flood damage assessment using synthetic aperture radar remote sensing imagery

Computer-Aided Civil and Infrastructure Engineering, EarlyView.
Abstract Most post‐disaster damage classifiers perform best when destructive forces leave clear spectral or structural signatures. However, these signatures are often subtle or absent after inundation, where damage may be nonstructural and difficult to detect.
Yu‐Hsuan Ho, Ali Mostafavi
wiley   +1 more source

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems

, 2022
Han Gao, Matthew J. Zahr, Jianxun Wang
openalex   +2 more sources

Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands

International Journal of Geo-Engineering
The accurate modeling of water and heat transport in soils is crucial for both geo-environmental and geothermal engineering. Traditional modeling methods are problematic because they require well-defined boundaries and initial conditions.
Yuan Feng, Jongwan Eun, Seunghee Kim, Yong-Rak Kim   +3 more
doaj   +1 more source

Quasi‐invariance of Gaussian measures for the 3d$3d$ energy critical nonlinear Schrödinger equation

Communications on Pure and Applied Mathematics, Volume 78, Issue 12, Page 2305-2353, December 2025.
Abstract We consider the 3d$3d$ energy critical nonlinear Schrödinger equation with data distributed according to the Gaussian measure with covariance operator (1−Δ)−s$(1-\Delta)^{-s}$, where Δ$\Delta$ is the Laplace operator and s$s$ is sufficiently large. We prove that the flow sends full measure sets to full measure sets. We also discuss some simple
Chenmin Sun, Nikolay Tzvetkov
wiley   +1 more source

A multi-layer neural network-based evaluation of MHD radiative heat transfer in Eyring–Powell fluid model

Heliyon
In the modern era, artificial intelligence (AI) has been applied as one of the transformative factors for scientific research in many fields that could provide new solutions to extremely complicated and complex physical models.
Muflih Alhazmi, Zahoor Shah, Muhammad Asif Zahoor Raja, Nafisa A. Albasheir, Maryam Jawaid   +4 more
doaj   +1 more source

Seismic Denoising by Deep Learning From Natural Repeating Earthquakes

Journal of Geophysical Research: Machine Learning and Computation, Volume 2, Issue 4, December 2025.
Abstract Effective seismic waveform denoising is crucial for advancing our understanding of Earth's subsurface structures and dynamics. Recently, deep learning‐based denoising methods have emerged and shown remarkable performance in improving the signal‐to‐noise ratio (SNR) of seismic waveforms.
Feiyi Wang, Yi Yang, Jianwei Ma
wiley   +1 more source

Multi-Output Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Uncertainties

, 2022
Mingyuan Yang, John T. Foster
openalex   +2 more sources