Results 111 to 120 of about 480,204 (279)
Energy Science &Engineering, EarlyView.ABSTRACT The turbulent flow between coaxial cylinders, particularly when the inner cylinder is rotating, exhibits complex hydrodynamic and heat transfer behaviors critical for various engineering applications. Although many previous studies have simplified these systems with idealized assumptions, real‐world scenarios involve mixed convection and ...
Tohid Adibi +5 more
wiley +1 more source
Tsirelson bounds for quantum correlations with indefinite causal order. [PDF]
Nat CommunLiu Z, Chiribella G.
europepmc +1 more source
Incremental Model Order Reduction of Smoothed‐Particle Hydrodynamic Simulations
International Journal for Numerical Methods in Fluids, EarlyView.The paper presents the development of an incremental singular value decomposition strategy for compressing time‐dependent particle simulation results, addressing gaps in the data matrices caused by temporally inactive particles. The approach reduces memory requirements by about 90%, increases the computational effort by about 10%, and preserves the ...
Eduardo Di Costanzo +3 more
wiley +1 more source
Number Theory and Polynomials: The Mahler measure of algebraic numbers: a survey [PDF]
, 2008 Chris Smyth
openalex
Journal of Forecasting, EarlyView.ABSTRACT This paper proposes a multilayer artificial neural network (ANN) method to predict the probability of default (PD) within a survival analysis framework. The ANN method captures hidden interconnections among covariates that influence PD, potentially leading to improved predictive performance compared to both logit and skewed logit models.
Yiannis Dendramis +2 more
wiley +1 more source
A General Approach to Dropout in Quantum Neural Networks
Advanced Quantum Technologies, EarlyView., 2023 Randomly dropping artificial neurons and all their connections in the training phase reduces overfitting issues in classical neural networks, thus improving performances on previously unseen data. The authors introduce different dropout strategies applied to quantum neural networks, learning models based on parametrized quantum circuits.
Francesco Scala +3 more
wiley +1 more source
A Learning Model with Memory in the Financial Markets
International Journal of Finance &Economics, EarlyView.ABSTRACT Learning is central to a financial agent's aspiration to gain persistent strategic advantage in asset value maximisation. The implicit mechanism that transforms this aspiration into an observed value gain is the speed of error corrections (demonstrating, an agent's speed of learning) whilst facing increased uncertainty.
Shikta Singh +6 more
wiley +1 more source
Benefits of Open Quantum Systems for Quantum Machine Learning
Advanced Quantum Technologies, EarlyView., 2023 Quantum machine learning (QML), poised to transform data processing, faces challenges from environmental noise and dissipation. While traditional efforts seek to combat these hindrances, this perspective proposes harnessing them for potential advantages. Surprisingly, under certain conditions, noise and dissipation can benefit QML.
María Laura Olivera‐Atencio +2 more
wiley +1 more source