Results 11 to 20 of about 223,298 (311)
Surrogate models for linear response
Physical Review ResearchLinear response theory is a well-established method in physics and chemistry for exploring excitations of many-body systems. In particular, the quasiparticle random-phase approximation (QRPA) provides a powerful microscopic framework by building ...
L. Jin +4 more
doaj +3 more sources
Universal Prediction Distribution for Surrogate Models [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2017 The use of surrogate models instead of computationally expensive simulation codes is very convenient in engineering. Roughly speaking, there are two kinds of surrogate models: the deterministic and the probabilistic ones. These last are generally based on Gaussian assumptions.
Ben Salem, Malek +3 more
openaire +6 more sources
Convergence of Weak-SINDy Surrogate Models
SIAM Journal on Applied Dynamical SystemsIn this paper, we give an in-depth error analysis for surrogate models generated by a variant of the Sparse Identification of Nonlinear Dynamics (SINDy) method. We start with an overview of a variety of non-linear system identification techniques, namely, SINDy, weak-SINDy, and the occupation kernel method.
Benjamin P. Russo, M. Paul Laiu
openaire +4 more sources
Utilising Surrogate Models to Approximate Cardiac Potentials when Solving Inverse Problems via Bayesian Techniques. [PDF]
Comput Cardiol (2010), 2022 Solving inverse problems is computationally expensive, if not infeasible, under specific scenarios. For example, many forward solutions are required when solving inverse problems using Bayesian techniques.
Kamalakkannan A, Johnston P, Johnston B.
europepmc +2 more sources
A surrogate FRAX model for Pakistan [PDF]
Archives of Osteoporosis, 2021 Abstract Summary A surrogate FRAX® model for Pakistan has been constructed using age-specific hip fracture rates for Indians living in Singapore and age-specific mortality rates from Pakistan.
Naureen, G. +11 more
openaire +6 more sources
Detecting Adversarial Examples Using Surrogate Models
Machine Learning and Knowledge Extraction, 2023 Deep Learning has enabled significant progress towards more accurate predictions and is increasingly integrated into our everyday lives in real-world applications; this is true especially for Convolutional Neural Networks (CNNs) in the field of image ...
Borna Feldsar +2 more
doaj +1 more source
Predicting Saltwater Intrusion into Coastal Aquifers Using Support Vector Regression Surrogate Models [PDF]
آب و فاضلاب, 2020 The prediction of the intrusion of saline water into coastal aquifers as a result of changing the amount of groundwater extractions is a prerequisite for managing groundwater.
Fatemeh Fa'al +2 more
doaj +1 more source
Applied Sciences, 2022 Natural disasters are unavoidable and can cause serious damage to bridges, which may lead to catastrophic losses, both human and economic. Therefore, the assessment of bridges exposed to these events is of paramount importance to identify possible ...
Carlos Mendoza Cabanzo +3 more
doaj +1 more source
Applied Sciences, 2021 Recent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored.
Ramin Ghiasi +5 more
doaj +1 more source
Pre-training method in the tasks of obtaining surrogate models of gas turbine units for gas turbine electric power stations [PDF]
E3S Web of Conferences, 2023 This article focuses on the application of pre-training methods in the task of synthesizing surrogate models. The article emphasizes that pre-training significantly improves the accuracy of surrogate models and speeds up their creation process.
Kilin Grigory +3 more
doaj +1 more source