Results 71 to 80 of about 767,107 (236)
Zhongguo Jianchuan Yanjiu, 2023 ObjectivesThe key to uncertainty design optimization (UDO) is uncertainty quantification (UQ), but the traditionally used Monte Carlo (MC) method can be time-consuming and computationally expensive.
Xiao WEI, Heng LI, Chenran HUANG
doaj +1 more source
MUSE: Modularizing Unsupervised Sense Embeddings
, 2018 This paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given contexts.
Brown, André EX +11 more
core +2 more sources
Stochastic Collocation With Non-Gaussian Correlated Process Variations: Theory, Algorithms, and Applications [PDF]
IEEE Transactions on Components, Packaging, and Manufacturing Technology, 2018 Stochastic spectral methods have achieved a great success in the uncertainty quantification of many engineering problems, including variation-aware electronic and photonic design automation. State-of-the-art techniques employ generalized polynomial-chaos
Chunfeng Cui, Zheng Zhang
semanticscholar +1 more source
Advanced Intelligent Discovery, EarlyView.CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian +12 more
wiley +1 more source
MathematicsThis paper introduces a novel numerical technique for solving fractional stochastic differential equations with neutral delays. The method employs a stepwise collocation scheme with Jacobi poly-fractonomials to consider unknown stochastic processes.
Afshin Babaei +4 more
doaj +1 more source
Robust Trajectory Optimization of a Ski Jumper for Uncertainty Influence and Safety Quantification
Proceedings, 2018 This paper deals with the development of a robust optimal control framework for a previously developed multi-body ski jumper simulation model by the authors.
Patrick Piprek, Florian Holzapfel
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Advanced Intelligent Discovery, EarlyView.We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang +4 more
wiley +1 more source
A mixed-method to numerical simulation of variable order stochastic advection diffusion equations
Alexandria Engineering JournalThe study of stochastic problems is very important and there is an increasing demand for investigating the behavior of a number of sophisticated dynamical systems in different areas of science as well as in engineering and finance.
H. Jafari +3 more
doaj +1 more source
Motion Planning of Uncertain Ordinary Differential Equation Systems [PDF]
, 2011 This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and under-actuated dynamical systems described by ordinary differential equations.
Hays, Joe +3 more
core +1 more source
, 2018 A variety of methods is available to quantify uncertainties arising with\-in the modeling of flow and transport in carbon dioxide storage, but there is a lack of thorough comparisons.
Barth, Andrea +10 more
core +1 more source