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Building Surrogate Models by Integrating Limited Simulation Data and Shape Prior Knowledge Via Adaptive Gradient Sampling Strategy

https://doi.org/10.2139/ssrn.4664119
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Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
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An Overview of Gradient-Enhanced Metamodels with Applications
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Building surrogate models for engineering problems by integrating limited simulation data and monotonic engineering knowledge
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A screening-based gradient-enhanced Gaussian process regression model for multi-fidelity data fusion
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A screening-based gradient-enhanced Kriging modeling method for high-dimensional problems
inferredcites →https://doi.org/10.1016/j.cma.2020.112861openalex-extracted
Optimization of expensive black-box problems via Gradient-enhanced Kriging
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Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization
inferredcites →https://doi.org/10.1016/j.cma.2023.116194openalex-extracted
Enhanced Kriging leave-one-out cross-validation in improving model estimation and optimization
inferredcites →https://doi.org/10.1016/j.engappai.2022.105724openalex-extracted
Reconstruction of hydrofoil cavitation flow based on the chain-style physics-informed neural network
inferredcites →https://doi.org/10.1016/j.neucom.2016.12.038openalex-extracted
A survey of deep neural network architectures and their applications
inferredcites →https://doi.org/10.1080/08982110701580930openalex-extracted
Modeling Data from Computer Experiments: An Empirical Comparison of Kriging with MARS and Projection Pursuit Regression
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Surrogate model of hybridized numerical relativity binary black hole waveforms
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Estimating Shape Constrained Functions Using Gaussian Processes
inferredcites →https://doi.org/10.1137/17m1153157openalex-extracted
Finite-Dimensional Gaussian Approximation with Linear Inequality Constraints
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Approximation Algorithms for NP-Hard Problems
inferredcites →https://doi.org/10.1162/evco_a_00294openalex-extracted
Shape-Constrained Symbolic Regression—Improving Extrapolation with Prior Knowledge
inferredcites →https://doi.org/10.1504/ijmmno.2013.055204openalex-extracted
A literature survey of benchmark functions for global optimisation problems
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Un-tethered Test Capability: Improving Airborne Test Efficiency
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Oil Spill Detection with Multiscale Conditional Adversarial Networks with Small-Data Training
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Impacts of Data Quantity and Quality on Model Calibration: Implications for Model Parameterization in Data-Scarce Catchments
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