Results 71 to 80 of about 1,345 (197)

Monetary Policy and Wealth Effects: The Role of Risk and Heterogeneity

The Journal of Finance, EarlyView.
ABSTRACT We study the role of asset revaluation in the monetary transmission mechanism. We build an analytical heterogeneous‐agents model with two main ingredients: (i) rare disasters and (ii) heterogeneous beliefs. The model captures time‐varying risk premia and precautionary savings in a setting that nests the textbook New Keynesian model.
NICOLAS CARAMP, DEJANIR H. SILVA
wiley   +1 more source

Monitoring of water volume in a porous reservoir using seismic data: Validation of a numerical model with a field experiment

Near Surface Geophysics, Volume 24, Issue 2, Page 110-127, April 2026.
Abstract As global groundwater levels continue to decline rapidly, there is a growing need for advanced techniques to monitor and manage aquifers effectively. This study focuses on validating a numerical model using seismic data from a small‐scale experimental setup designed to estimate water volume in a porous reservoir.
Mahnaz Khalili, Bojan Brodic, Peter Göransson, Suvi Heinonen, Jan S. Hesthaven, Antti Pasanen, Marko Vauhkonen, Rahul Yadav, Timo Lähivaara   +8 more
wiley   +1 more source

Reduced-Order Model for Performance Simulation and Conceptual Design of Rocket-Type Pulse Detonation Engines

Aerospace
A model-based method has been developed for the performance simulation and conceptual design of rocket-type pulse detonation engines (PDEs). A reduced-order model (ROM) has been generated based on the high order singular value decomposition of a data ...
Luis Sánchez de León, Francisco Sastre, Elena Martin, Angel Velazquez   +3 more
doaj   +1 more source

Space Correlation Constrained Physics Informed Neural Network for Seismic Tomography

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Physics‐informed neural networks (PINNs) integrate physical constraints with neural architectures and leverage their nonlinear fitting capabilities to solve complex inverse problems. Tomography serves as a classic example, aiming to reconstruct subsurface velocity models to improve seismic exploration.
Yonghao Wang, Zhuo Jia, Bowu Jiang, Wenkai Lu   +3 more
wiley   +1 more source

Unifying and extending diffusion models through PDEs for solving inverse problems

Computer Methods in Applied Mechanics and Engineering
Diffusion models have emerged as powerful generative tools with applications in computer vision and scientific machine learning (SciML), where they have been used to solve large-scale probabilistic inverse problems. Traditionally, these models have been derived using principles of variational inference, denoising, statistical signal processing, and ...
Agnimitra Dasgupta, Alexsander Marciano da Cunha, Ali Fardisi, Mehrnegar Aminy, Brianna Binder, Bryan Shaddy, Assad A. Oberai   +6 more
openaire   +2 more sources

Macroscopic Market Making Games

Mathematical Finance, Volume 36, Issue 2, Page 352-373, April 2026.
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

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

LocRes–PINN: A Physics–Informed Neural Network with Local Awareness and Residual Learning

Computation
Physics–Informed Neural Networks (PINNs) have demonstrated efficacy in solving both forward and inverse problems for nonlinear partial differential equations (PDEs).
Tangying Lv, Wenming Yin, Hengkai Yao, Qingliang Liu, Yitong Sun, Kuan Zhao, Shanliang Zhu   +6 more
doaj   +1 more source

Homogenization With Guaranteed Bounds via Primal‐Dual Physically Informed Neural Networks

International Journal for Numerical Methods in Engineering, Volume 127, Issue 6, 30 March 2026.
ABSTRACT Physics‐informed neural networks (PINNs) have shown promise in solving partial differential equations (PDEs) relevant to multiscale modeling, but they often fail when applied to materials with discontinuous coefficients, such as media with piecewise constant properties. This paper introduces a dual formulation for the PINN framework to improve
Liya Gaynutdinova, Ondřej Rokoš, Martin Doškář, Ivana Pultarová   +3 more
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