Results 81 to 90 of about 1,606 (214)
Efficient Deconvolution in Populational Inverse Problems
International Journal for Numerical Methods in Engineering, Volume 127, Issue 9, 15 May 2026.ABSTRACT This work is focused on the inversion task of inferring the distribution over parameters of interest, leading to multiple sets of observations. The potential to solve such distributional inversion problems is driven by the increasing availability of data, but a major roadblock is blind deconvolution, arising when the observational noise ...
Arnaud Vadeboncoeur +2 more
wiley +1 more source
A polynomial chaos method for arbitrary random inputs using B-splines [PDF]
, 2020 Isogeometric analysis which extends the finite element method through the usage of B-splines has become well established in engineering analysis and design procedures.
Beer, M, Eckert, C, Spanos, PD
core +1 more source
Encoding Cumulation to Learn Perturbative Nonlinear Oscillatory Dynamics
Advanced Science, Volume 13, Issue 25, 4 May 2026.Weak nonlinearities critically shape the long term behavior of oscillatory systems but are difficult to identify from data. A data‐driven framework is introduced to infer governing equations of weakly nonlinear oscillators from sparse and noisy observations.
Teng Ma +5 more
wiley +1 more source
Journal of Physics: Conference Series, 2011 Statistical methodologies based on surrogate-models have proved to be an efficient approach to quantify the physical properties of turbulent flows. The underlying idea is to parametrize the space of possible solutions via a computationally inexpensive approximation model, which is then used to generate samples for the statistical tool at hand.
M., Meldi, Lucor, Didier, P., Sagaut
openaire +2 more sources
Uncertainty quantification (UQ)
, 2011 This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece.
Karniadakis, GE +1 more
core
Modeling Uncertainty in Steady State Diffusion Problems via Generalized Polynomial Chaos
, 2002 We present a generalized polynomial chaos algorithm for the solution of stochastic elliptic partial differential equations suject to uncertain inputs. In particular, we focus on the solution of the Poisson equation with random diffusivity, forcing and ...
George Em Karniadakis, Dongbin Xiu
core +1 more source
Machine Learning‐Driven Variability Analysis of Process Parameters for Semiconductor Manufacturing
Advanced Intelligent Systems, Volume 8, Issue 5, May 2026.This research presents a machine learning approach that integrates nonlinear variation decomposition (NLVD) with statistical techniques to quantify the contribution of individual unit processes to performance and variance of figure of merit (FoM) at the LOT level.
Sinyeong Kang +6 more
wiley +1 more source
, 2013 The knowledge of uncertain parameter distributions is often required to investigate any typical stochastic problem. It may be possible to directly measure uncertain parameters but this is often quite easier to identifying these parameters from system ...
Marburg, S., Sepahvand, K.
core +1 more source
International Journal for Numerical Methods in Fluids, Volume 98, Issue 5, Page 557-577, May 2026.This study investigates the impact of uncertain parameters on Navier–Stokes equations coupled with heat transfer using the Intrusive Polynomial Chaos Method (IPCM). Sensitivity equations are formulated for key input parameters, such as viscosity and thermal diffusivity, and solved numerically using the Finite Element‐Volume method.
N. Nouaime +3 more
wiley +1 more source
SENSITIVITY ANALYSIS OF IMAGE CLASSIFICATION MODELS USING GENERALIZED POLYNOMIAL CHAOS
Proceedings of the 6th International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP)Integrating advanced communication protocols in production has accelerated the adoption of data-driven predictive quality methods, notably machine learning (ML) models. However, ML models in image classification often face significant uncertainties arising from model, data, and domain shifts.
Lukas Bahr +5 more
openaire +3 more sources