Results 281 to 290 of about 24,699,198 (331)
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A Deep Learning Surrogate Model for Topology Optimization
IEEE transactions on magnetics, 2021In this work, a topology optimization procedure is proposed and applied to the TEAM 25 problem, i.e., a model of a die press with an electromagnet for orientation of magnetic powder.
S. Barmada +4 more
semanticscholar +1 more source
, 2021
To mitigate the risk of manufacturing defects of thick composite component and improve the efficiency of this process, a multi-objective optimization approach was proposed to optimize the cure process using the multi-field coupled model, surrogate model ...
Zhenyi Yuan +7 more
semanticscholar +1 more source
To mitigate the risk of manufacturing defects of thick composite component and improve the efficiency of this process, a multi-objective optimization approach was proposed to optimize the cure process using the multi-field coupled model, surrogate model ...
Zhenyi Yuan +7 more
semanticscholar +1 more source
Hull optimization of an underwater vehicle based on dynamic surrogate model
, 2021A parallel multidisciplinary optimization design is proposed for the lines design of an underwater vehicle. Resistance and energy consumption are concerned about in obtaining the optimized lines.
W. Luo +3 more
semanticscholar +1 more source
Protecting SLAs with surrogate models
Proceedings of the 2nd International Workshop on Principles of Engineering Service-Oriented Systems, 2010In this paper, we propose the use of surrogate models to avoid or limit violations of the service level agreements (protect SLAs) of enterprise applications executed within virtualized data centers (VDCs).Modern enterprise services are delivered along with service level agreements (SLAs) that formalize the expected quality of service, and define ...
Alessio Gambi +2 more
openaire +2 more sources
Water Resources Research, 1972
To improve the accuracy and completeness of a data base is expensive. Mathematical models and digital computer simulation techniques make a quantitative evaluation of the worth of improving the data base possible by empirical sensitivity analysis. Triangular and log triangular error distributions have been found suitable for Monte Carlo experiments to ...
openaire +1 more source
To improve the accuracy and completeness of a data base is expensive. Mathematical models and digital computer simulation techniques make a quantitative evaluation of the worth of improving the data base possible by empirical sensitivity analysis. Triangular and log triangular error distributions have been found suitable for Monte Carlo experiments to ...
openaire +1 more source
2019
This Chapter presents the first key component of BO, that is, the probabilistic surrogate model. Section 3.1 is focused on Gaussian processes (GPs); Sect. 3.2 introduces the sequential optimization method known as Thompson sampling, also based on GP; finally, Sect. 3.3 presents other probabilistic models which might represent, in some cases, a suitable
Francesco Archetti, Antonio Candelieri
openaire +1 more source
This Chapter presents the first key component of BO, that is, the probabilistic surrogate model. Section 3.1 is focused on Gaussian processes (GPs); Sect. 3.2 introduces the sequential optimization method known as Thompson sampling, also based on GP; finally, Sect. 3.3 presents other probabilistic models which might represent, in some cases, a suitable
Francesco Archetti, Antonio Candelieri
openaire +1 more source
Surrogate Models for Coupled Microgrids
2019We consider the operation of coupled microgrids. Each microgrid consists of a number of residential energy systems, each including an energy storage device. The goal is to determine an optimal energy exchange between the microgrids, which results in a two-level optimization problem.
Grundel, S. +2 more
openaire +2 more sources
, 2020
Surrogate models can emulate physics-based building energy simulation with a machine learning model trained on simulation input and output data. The trained model is extremely fast to run, allowing us to estimate simulation outcomes for thousands of ...
Paul Westermann +2 more
semanticscholar +1 more source
Surrogate models can emulate physics-based building energy simulation with a machine learning model trained on simulation input and output data. The trained model is extremely fast to run, allowing us to estimate simulation outcomes for thousands of ...
Paul Westermann +2 more
semanticscholar +1 more source
, 2020
Surrogate models, which have become a popular approach to oil-reservoir production-optimization problems, use a computationally inexpensive approximation function to replace the computationally expensive objective function computed by a numerical ...
Guodong Chen +7 more
semanticscholar +1 more source
Surrogate models, which have become a popular approach to oil-reservoir production-optimization problems, use a computationally inexpensive approximation function to replace the computationally expensive objective function computed by a numerical ...
Guodong Chen +7 more
semanticscholar +1 more source
International journal for numerical and analytical methods in geomechanics (Print), 2020
Efficient evaluation of slope stability is a frontier in geo‐disaster prevention fields. While many slope stability evaluation methods, ranging from deterministic to probabilistic, have been proposed, reliability methods are particularly advantageous as ...
B. Zhu, T. Hiraishi, H. Pei, Q. Yang
semanticscholar +1 more source
Efficient evaluation of slope stability is a frontier in geo‐disaster prevention fields. While many slope stability evaluation methods, ranging from deterministic to probabilistic, have been proposed, reliability methods are particularly advantageous as ...
B. Zhu, T. Hiraishi, H. Pei, Q. Yang
semanticscholar +1 more source