Results 101 to 110 of about 35,012 (233)

A model-consistent data-driven computational strategy for PDE joint inversion problems

Journal of Computational Physics
The task of simultaneously reconstructing multiple physical coefficients in partial differential equations (PDEs) from observed data is ubiquitous in applications. In this work, we propose an integrated data-driven and model-based iterative reconstruction framework for such joint inversion problems where additional data on the unknown coefficients are ...
Kui Ren, Lu Zhang
openaire   +2 more sources

Impact of Uncertain Parameters on Navier–Stokes Equations With Heat Transfer via Polynomial Chaos Expansion

International Journal for Numerical Methods in Fluids, Volume 98, Issue 5, Page 557-577, May 2026.
This study investigates the impact of uncertain parameters on Navier–Stokes equations coupled with heat transfer using the Intrusive Polynomial Chaos Method (IPCM). Sensitivity equations are formulated for key input parameters, such as viscosity and thermal diffusivity, and solved numerically using the Finite Element‐Volume method.
N. Nouaime, B. Després, M. A. Puscas, C. Fiorini   +3 more
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

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

Quasi second-order methods for PDE-constrained forward and inverse problems

, 2021
Zehnder, Jonas
openalex   +1 more source

Direct Numerical Simulation of Magnetohydrodynamic Slip‐Flow Past a Stretching Surface Using Physics‐Informed Neural Network

Heat Transfer, Volume 55, Issue 3, Page 1674-1682, May 2026.
ABSTRACT Traditional numerical methods, such as finite difference methods (FDM), finite element methods (FEM), and spectral methods, often face meshing challenges and high computational cost for solving nonlinear coupled differential equations. Machine learning techniques, specifically Physics‐informed machine learning, address these obstacles by ...
Ahmad, Feroz Soomro, Husna Zafar
wiley   +1 more source

Pharmacological and Mechanistic Interventions for Cognitive Impairment Associated With Schizophrenia: A Review of Registered Clinical Trials

Acta Psychiatrica Scandinavica, Volume 153, Issue 5, Page 402-431, May 2026.
ABSTRACT Background Schizophrenia is characterized by positive, negative, and cognitive symptoms. Current pharmacological treatments often fail to address cognitive deficits. In this review of clinical trials, we aim to identify studies that explore neurobiological (non‐psychological) strategies to address Cognitive Impairment Associated with ...
Bahareh Peyrovian, Lena Palaniyappan, Theodore T. Kolivakis, Howard C. Margolese   +3 more
wiley   +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

Are physiological oscillations physiological?

The Journal of Physiology, Volume 604, Issue 9, Page 3672-3693, 1 May 2026.
Abstract figure legend Mechanisms and functions of physiological oscillations. Abstract Despite widespread and striking examples of physiological oscillations, their functional role is often unclear. Even glycolysis, the paradigm example of oscillatory biochemistry, has seen questions about its oscillatory function.
Lingyun (Ivy) Xiong, Alan Garfinkel
wiley   +1 more source

Latent Twins

Machine Learning: Science and Technology
Over the past decade, scientific machine learning has transformed the development of mathematical and computational frameworks for analyzing, modeling, and predicting complex systems.
Matthias Chung, Deepanshu Verma, Max Collins, Amit N Subrahmanya, Varuni Katti Sastry, Vishwas Rao   +5 more
doaj   +1 more source