Results 81 to 90 of about 85,369,012 (181)

Inverse Problems Using Reduced Basis Method [PDF]

, 2014
Inverse Problems is a field of great interest for many applications, such as parameter identification and image reconstruction. The underlying models of inverse problems in many applications often involve Partial Differential Equations (PDEs).
Gralla, Phil
core   +1 more source

Feedback Linearisation with State Constraints

International Journal of Robust and Nonlinear Control, Volume 36, Issue 17, Page 7579-7595, 25 November 2026.
ABSTRACT Feedback Linearisation (FBL) is a widely used technique that applies feedback laws to transform input‐affine nonlinear control systems into linear control systems, allowing for the use of linear controller design methods such as pole placement.
Songlin Jin, Yuanbo Nie, Morgan Jones
wiley   +1 more source

Regularized fixed-point iterations for nonlinear inverse problems

, 2022
S.1-22In this paper, we introduce a derivative-free, iterative method for solving nonlinear ill-posed problems Fu = y, where instead of y, noisy data y(delta) with parallel to y - y(delta)parallel to Y is a nonlinear operator between Hilbert spaces X ...
Pereverzyev, S.S., Pinnau, R., Siedow, N.   +2 more
core   +1 more source

A New Physics‐Constrained Neural Network Approach to Retrieve CH4 Vertical Profiles From Hyperspectral Infrared Data

Journal of Geophysical Research: Atmospheres, Volume 131, Issue 21, 16 November 2026.
Abstract Methane (CH4 ${\text{CH}}_{4}$) is a critical greenhouse gas, and accurate monitoring of its vertical distribution is essential for understanding its radiative impact and identifying emission sources. This paper introduces a novel methodology using a physics‐constrained neural network based on soft constraints to retrieve CH4 ${\text{CH}}_{4}$
Rocco Giosa, Marco D’Emilio, Pamela Pasquariello, Guido Masiello, Carmine Serio, Maria Ragosta, Francesco Carbone, Christian Natale Gencarelli, Italia De Feis, Diana Carolina Fonnegra Mora, Lorenzo Cassini, Raffaele Inglese, Giuliano Liuzzi   +12 more
wiley   +1 more source

Uncertainty quantification and weak approximation of an elliptic inverse problem [PDF]

, 2011
We consider the inverse problem of determining the permeability from the pressure in a Darcy model of flow in a porous medium. Mathematically the problem is to find the diffusion coefficient for a linear uniformly elliptic partial differential equation ...
Worthington, Claire, Robinson, Stewart, Davis, Neil D., Radnor, Zoe, Cooke, Matthew W, Burgess, Nicola, Cooke, Matthew, M. Dashti, Davies, Neil, Dashti, M., A. M. Stuart, Masoumeh Dashti (4463344), Worthington, Claire H, A M Stuart (13396491), Stuart, A. M.   +14 more
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The Effect of Acute Beetroot Juice and Caffeine Supplementation on Specific Judo Performance During a Simulated Competition Day in Judo Athletes

European Journal of Sport Science, Volume 26, Issue 11, November 2026.
ABSTRACT Beetroot juice (BJ) and caffeine (CAF) are well‐established ergogenic aids for exercise performance. However, little is known about their efficacy and interaction within the specific context of a judo competition day. This study investigated the acute effects of BJ, CAF, and their combination (BJCAF) on judo‐specific performance across a ...
Sefer Karabağ, Heidar Sajedi, Onur Güler, Vahdet Alaeddinoğlu, Ergün Çakır, Davar Khodadadi, Selahattin Akpınar   +6 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

Jacobian-AIME: A Local and Functional Approach to Interpreting Nonlinear Maps for Physics-Informed Neural Networks

IEEE Access
We propose Jacobian-AIME, a novel explanation framework for Physics-Informed Neural Networks (PINNs). PINNs map coordinates to physical fields governed by PDEs.
Kosuke Yano, Takafumi Nakanishi, Soichiro Ikuno   +2 more
doaj   +1 more source

Agentic artificial intelligence in mechanics of materials: A review of building tailored models on demand

GAMM-Mitteilungen, Volume 49, Issue 4, November 2026.
Abstract In mechanics of materials, many powerful methods are accessible only to experts. Constitutive models, for example, describe the relation between stress and strain in materials and are a core ingredient of simulations from structural engineering to biomechanics. Yet building and using such models requires specialized expertise—a barrier for the
Marius Tacke, Matthias Busch, Kevin Linka, Roland Aydin, Christian Cyron   +4 more
wiley   +1 more source

Initial and boundary value problems in two and three dimensions [PDF]

, 2010
This thesis: (a) presents the solution of several boundary value problems (BVPs) for the Laplace and the modified Helmholtz equations in the interior of an equilateral triangle; (b) presents the solution of the heat equation in the interior of an ...

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