Results 21 to 30 of about 24,324,331 (382)
Surrogate modeling of structural seismic response using probabilistic learning on manifolds
Earthquake Engineering & Structural Dynamics, 2023 Nonlinear response history analysis (NLRHA) is generally considered to be a reliable and robust method to assess the seismic performance of buildings under strong ground motions. While NLRHA is fairly straightforward to evaluate individual structures for
Kuanshi Zhong +3 more
semanticscholar +1 more source
Spatio-Temporal Gradient Enhanced Surrogate Modeling Strategies
Mathematical and Computational Applications, 2023 This research compares the performance of space-time surrogate models (STSMs) and network surrogate models (NSMs). Specifically, when the system response varies over time (or pseudo-time), the surrogates must predict the system response.
Johann M. Bouwer +2 more
doaj +1 more source
Surrogate Modeling for Bayesian Optimization Beyond a Single Gaussian Process [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Bayesian optimization (BO) has well-documented merits for optimizing black-box functions with an expensive evaluation cost. Such functions emerge in applications as diverse as hyperparameter tuning, drug discovery, and robotics.
Qin Lu +3 more
semanticscholar +1 more source
Surrogate modeling for the climate sciences dynamics with machine learning and data assimilation
Frontiers in Applied Mathematics and Statistics, 2023 The outstanding breakthroughs of deep learning in computer vision and natural language processing have been the horn of plenty for many recent developments in the climate sciences.
M. Bocquet
semanticscholar +1 more source
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data [PDF]
Computer Methods in Applied Mechanics and Engineering, 2019 Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation using polynomials into a finite-dimensional algebraic system.
Luning Sun +3 more
semanticscholar +1 more source
Water Resources Research, 2023 Inverse modeling is usually necessary for prediction of subsurface flows, which is beneficial to characterize underground geologic properties and reduce prediction uncertainty.
Nanzhe Wang +2 more
doaj +1 more source
An artificial-neural-network-based surrogate modeling workflow for reactive transport modeling
Petroleum Research, 2022 Process-based reactive transport modeling (RTM) integrates thermodynamic and kinetically controlled fluid-rock interactions with fluid flow through porous media in the subsurface and surface environment.
Yupeng Li, Peng Lu, Guoyin Zhang
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Surrogate Modeling of Electrical Machine Torque Using Artificial Neural Networks
IEEE Access, 2020 Machine learning and artificial neural networks have shown to be applicable in modeling and simulation of complex physical phenomena as well as creating surrogate models trained with physics-based simulation data for numerous applications that require ...
Mikko Tahkola +4 more
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Modelling, 2023 Complex engineering models are typically computationally demanding and defined by a high-dimensional parameter space challenging the comprehensive exploration of parameter effects and design optimization.
Corey Arndt +4 more
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Cost-Efficient Bi-Layer Modeling of Antenna Input Characteristics Using Gradient Kriging Surrogates
IEEE Access, 2020 Over the recent years, surrogate modeling has been playing an increasing role in the design of antenna structures. The main incentive is to mitigate the issues related to high cost of electromagnetic (EM)-based procedures.
Anna Pietrenko-Dabrowska +2 more
doaj +1 more source