Results 71 to 80 of about 746,368 (310)
A Variational Beam Model for Failure of Cellular and Truss‐Based Architected Materials
Advanced Engineering Materials, EarlyView., 2023 Herein, a versatile and efficient beam modeling framework is developed to predict the nonlinear response and failure of cellular, truss‐based, and woven architected materials. It enables the exploration of their design space and the optimization of their mechanical behavior in the nonlinear regime. A variational formulation of a beam model is presented
Konstantinos Karapiperis +3 more
wiley +1 more source
Advanced Engineering Materials, EarlyView., 2023 A novel method for tracking structural changes in gels using widely accessible microcomputed tomography is presented and validated for various hydro‐, alco‐, and aerogels. The core idea of the method is to track positions of micrometer‐sized tracer particles entrapped in the gel and relate them to the density of the gel network.
Anja Hajnal +3 more
wiley +1 more source
Learning Differential Invariants of Planar Curves [PDF]
arXiv, 2023 We propose a learning paradigm for the numerical approximation of differential invariants of planar curves. Deep neural-networks' (DNNs) universal approximation properties are utilized to estimate geometric measures. The proposed framework is shown to be a preferable alternative to axiomatic constructions.
arxiv
Population and Empirical PR Curves for Assessment of Ranking Algorithms [PDF]
arXiv, 2018 The ROC curve is widely used to assess the quality of prediction/classification/ranking algorithms, and its properties have been extensively studied. The precision-recall (PR) curve has become the de facto replacement for the ROC curve in the presence of imbalance, namely where one class is far more likely than the other class.
arxiv
Machine learning for moduli space of genus two curves and an application to isogeny based cryptography [PDF]
Journal of Algebraic Combinatorics, 61, 23 (2025)We use machine learning to study the moduli space of genus two curves, specifically focusing on detecting whether a genus two curve has $(n, n)$-split Jacobian. Based on such techniques, we observe that there are very few rational moduli points with small weighted moduli height and $(n, n)$-split Jacobian for $n=2, 3, 5$.
arxiv +1 more source
An infinite learning curve [PDF]
Nature, 2005 More scientists seek formal training beyond the PhD — for both on- and off-the bench skills.
openaire +3 more sources
Learning micro incision surgery without the learning curve
Indian Journal of Ophthalmology, 2008 We describe a method of learning micro incision cataract surgery painlessly with the minimum of learning curves. A large-bore or standard anterior chamber maintainer (ACM) facilitates learning without change of machine or preferred surgical technique ...
Thomas Ravi, Navin Shoba, Parikh Rajul
doaj
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Purpose This study compares the dosimetric accuracy of deep‐learning‐based MR synthetic CT (sCT) in brain radiotherapy between the Analytical Anisotropic Algorithm (AAA) and AcurosXB (AXB). Additionally, it proposes a novel metric to predict the dosimetric accuracy of sCT for individual post‐surgical brain cases.
Jeffrey C. F. Lui +3 more
wiley +1 more source
Multiple Yield Curve Modeling and Forecasting using Deep Learning [PDF]
ASTIN Bull. 54 (2024) 463-494This manuscript introduces deep learning models that simultaneously describe the dynamics of several yield curves. We aim to learn the dependence structure among the different yield curves induced by the globalization of financial markets and exploit it to produce more accurate forecasts.
arxiv +1 more source
Management Science, 2020 The expansive learning curve literature in operations management has established how various facets of prior experience improve average performance. In this paper, we explore how increased cumulative experience affects performance variability or consistency.
Hessam Bavafa, Jónas Oddur Jónasson
openaire +3 more sources