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
Surrogate modeling: tricks that endured the test of time and some recent developments
Structural and Multidisciplinary Optimization, 2021 Felipe A C Viana +2 more
exaly +2 more sources
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes [PDF]
Advances in Engineering Software, 2023 The Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems.
Paul Maxime Valentin Saves +8 more
semanticscholar +1 more source
Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings [PDF]
Design Automation Conference, 2023 Generative AI models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization, evaluating engineering metrics is crucial.
Binyang Song +4 more
semanticscholar +1 more source
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
Active-Learning-Driven Surrogate Modeling for Efficient Simulation of Parametric Nonlinear Systems [PDF]
Computer Methods in Applied Mechanics and Engineering, 2023 When repeated evaluations for varying parameter configurations of a high-fidelity physical model are required, surrogate modeling techniques based on model order reduction are desired.
Harshit Kapadia, Lihong Feng, P. Benner
semanticscholar +1 more source
Data-driven decision-focused surrogate modeling [PDF]
AIChE Journal, 2023 We introduce the concept of decision‐focused surrogate modeling for solving computationally challenging nonlinear optimization problems in real‐time settings.
Rishabh Gupta, Qi Zhang
semanticscholar +1 more source
An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials [PDF]
npj Computational Materials, 2022 The purpose of this work is the development of a trained artificial neural network for surrogate modeling of the mechanical response of elasto-viscoplastic grain microstructures.
M. S. Khorrami +6 more
semanticscholar +1 more source
Multi-fidelity surrogate modeling using long short-term memory networks [PDF]
Computer Methods in Applied Mechanics and Engineering, 2022 When evaluating quantities of interest that depend on the solutions to differential equations, we inevitably face the trade-off between accuracy and efficiency. Especially for parametrized, time dependent problems in engineering computations, it is often
Paolo Conti +3 more
semanticscholar +1 more source
A physics-informed deep neural network for surrogate modeling in classical elasto-plasticity [PDF]
Computers and geotechnics, 2022 In this work, we present a deep neural network architecture that can efficiently approximate classical elasto-plastic constitutive relations. The network is enriched with crucial physics aspects of classical elasto-plasticity, including additive ...
M. Eghbalian, M. Pouragha, R. Wan
semanticscholar +1 more source