Results 21 to 30 of about 181 (103)

Hitchhiker's Guide to the Swampland: The Cosmologist's Handbook to the String‐Theoretical Swampland Programme

Fortschritte der Physik, Volume 74, Issue 4, April 2026.
Abstract String theory has strong implications for cosmology, implying the absence of a cosmological constant, ruling out single‐field slow‐roll inflation, and that black holes decay. The origins of these statements are elucidated within the string‐theoretical swampland programme.
Kay Lehnert
wiley   +1 more source

Financial Time Series Uncertainty: A Review of Probabilistic AI Applications

Journal of Economic Surveys, Volume 40, Issue 2, Page 915-953, April 2026.
ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen, Tord Johan Espe, Kristoffer Grude, Morten Risstad, Rickard Sandberg   +4 more
wiley   +1 more source

Traces of Hecke operators on Drinfeld modular forms for GL2(Fq[T])$\operatorname{GL}_2(\mathbb {F}_q[T])$

Mathematika, Volume 72, Issue 2, April 2026.
Abstract In this paper, we study traces of Hecke operators on Drinfeld modular forms of level 1 in the case A=Fq[T]$A = \mathbb {F}_q[T]$. We deduce closed‐form expressions for traces of Hecke operators corresponding to primes of degree at most 2 and provide algorithms for primes of higher degree.
Sjoerd de Vries
wiley   +1 more source

Algebraic Multigrid Based Preconditioning Approaches for Generalized Continuum Models and Indirect Displacement Control Techniques

International Journal for Numerical Methods in Engineering, Volume 127, Issue 6, 30 March 2026.
ABSTRACT The contribution deals with algebraic multigrid (AMG) based preconditioning methods for the iterative solution of a coupled linear system of equations arising in numerical simulations of failure of quasi‐brittle materials using generalized continuum approaches.
Nasser Alkmim, Peter Gamnitzer, Alexander Dummer, Matthias Neuner, Günter Hofstetter   +4 more
wiley   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, Volume 36, Issue 24, 23 March 2026.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

Finite Element Analysis and ANN Validation of Mixed Convection Nanofluid Flow in a Star‐Shaped Corrugated Cavity With Multiple Heated Baffles

Engineering Reports, Volume 8, Issue 3, March 2026.
This study carefully examines the mixed convection heat transport properties of a water–Al2O3 nanofluid inside a corrugated chamber with heated baffles in the shape of a star. The effects of different Rayleigh numbers, nanoparticle volume fractions, and baffle placement are carefully evaluated, and the cavity is subjected to buoyancy forces. The finite
Rabia Salma, Sohail Nadeem, Jehad Alzabut, Muzamil Fatima, B. Zigta   +4 more
wiley   +1 more source

Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery

Med Research, Volume 2, Issue 1, Page 130-156, March 2026.
The integration of AI/ML into nanomedicine offers a transformative approach to therapeutic design and optimization. Unlike conventional empirical methods, AI/ML models (such as classification, regression, and neural networks) enable the analysis of complex clinical and formulation datasets to predict optimal nanoparticle characteristics and therapeutic
Rohan Chand Sahu, Sanchit Arora, Dinesh Kumar, Ashish Kumar Agrawal   +3 more
wiley   +1 more source

Implicit Third‐Order Peer Triplets with Variable Stepsizes for Gradient‐Based Solutions in Large‐Scale ODE‐Constrained Optimal Control

Optimal Control Applications and Methods, Volume 47, Issue 2, Page 570-588, March/April 2026.
Implicit third‐order Peer two‐step methods that are superconvergent for variable stepsizes have the potential to significantly improve the efficiency of solving large‐scale ODE‐constrained optimal control problems. These include real‐world applications in medical treatment planning for prostate cancer, such as the design of effective three‐dose drug ...
Jens Lang, Bernhard A. Schmitt
wiley   +1 more source

Methods Based on Polynomial Chaos for Quadratic Delay Differential Equations With Random Parameters

Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 1, March 2026.
ABSTRACT We consider systems of delay differential equations (DDEs), including a single delay and a quadratic right‐hand side. In a system, parameters are replaced by random variables to perform an uncertainty quantification. Thus the solution of the DDEs becomes a random process, which can be represented by a series of the generalised polynomial chaos.
Roland Pulch
wiley   +1 more source

On the Euler characteristic of S$S$‐arithmetic groups

Journal of the London Mathematical Society, Volume 113, Issue 3, March 2026.
Abstract We show that the sign of the Euler characteristic of an S$S$‐arithmetic subgroup of a simple algebraic group depends on the S$S$‐congruence completion only, except possibly in type 6D4${}^6 D_4$. Consequently, the sign is a profinite invariant for such S$S$‐arithmetic groups with the congruence subgroup property. This generalizes previous work
Holger Kammeyer, Giada Serafini
wiley   +1 more source