Bayesian optimization identifies cement-salt hydrate composites achieving up to five times higher specific energy than prior cement-based TCES materials, with LiCl-based formulations reaching 458 kJ/kg.
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Efficient Global Optimization of Expensive Black-Box Functions
16 Pith papers cite this work, alongside 7,799 external citations. Polarity classification is still indexing.
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PALM-Mean combines sign-aware piecewise-linear relaxations of locally important kernel terms with closed-form analytic bounds on the rest inside a reduced-space branch-and-bound framework, yielding valid lower bounds and ε-global convergence for GP posterior mean optimization.
KDE-AIS trains a Gaussian process and kernel density surrogate from shared evaluations to build an adaptive importance sampling proposal that converges to the zero-variance optimum for efficient failure probability estimation.
Assembly-like FEM updating performs ~95% of solves on subassemblies for 28% lower workload proxy effort with fidelity within 1% of global updating on experimental flexible wing data.
PRIOR replaces masking-based ordering pressure with predictive residual inference using level-wise predictors to produce well-ordered representations that maintain or improve performance across budgets, especially in discrete settings.
LLMs can forecast GPU kernel performance accurately enough to serve as selective surrogates, allowing kernel searches to consider more candidates and recover faster kernels under fixed GPU evaluation budgets.
BACO replaces direct black-box calls in collaborative optimization with Gaussian process surrogates at both subsystem and system levels, achieving lower objectives and near-zero constraint violations on MDO benchmarks and a CRM wing problem within limited evaluations.
Two frameworks for nonlinear equality constraints in gradient-enhanced local Bayesian optimization achieve deeper convergence with fewer function evaluations than previous constrained BO methods and SciPy/MATLAB quasi-Newton optimizers on unimodal problems with 2-30 variables.
Describes an LLM-orchestrated workflow that trains a surrogate on CAE data (R²=0.87), runs NSGA-II optimization, generates morphed geometries, and produces 35 compliant pedestrian-protection designs in a front-bumper case study.
A gradient-enhanced local Bayesian optimization framework that converges optimality as deeply as standard optimizers but with significantly fewer function evaluations on 2-40 dimensional unimodal problems, outperforming them under noisy gradients.
Reinforcement learning selects hyperparameters sequentially by learning from actual future validation loss reductions and outperforms SMBO methods on 50 datasets.
An adaptive target-variance scheme for Monte Carlo integration inside sEGO improves performance over fixed-variance and multi-start baselines on stochastic benchmarks and a tuned-mass-damper problem while enabling high stochastic dimension counts.
Systematic benchmarks on NACA0012, RAE2822, and ONERA M6 cases show derivative-free optimizers competitive with adjoint-based methods and stronger in higher dimensions.
Bayesian optimization with Gaussian processes unifies minimization, single-point saddle searches, and double-ended path searches on potential energy surfaces through a shared six-step surrogate loop using derivative observations and inverse-distance kernels.
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
citing papers explorer
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High-Throughput Bayesian Optimization of Cement-Salt Hydrates Composites for Seasonal Thermochemical Energy Storage
Bayesian optimization identifies cement-salt hydrate composites achieving up to five times higher specific energy than prior cement-based TCES materials, with LiCl-based formulations reaching 458 kJ/kg.
-
An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions
PALM-Mean combines sign-aware piecewise-linear relaxations of locally important kernel terms with closed-form analytic bounds on the rest inside a reduced-space branch-and-bound framework, yielding valid lower bounds and ε-global convergence for GP posterior mean optimization.
-
Surrogate-Guided Adaptive Importance Sampling for Failure Probability Estimation
KDE-AIS trains a Gaussian process and kernel density surrogate from shared evaluations to build an adaptive importance sampling proposal that converges to the zero-variance optimum for efficient failure probability estimation.
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A Paradigm Shift to Assembly-like Finite Element Model Updating
Assembly-like FEM updating performs ~95% of solves on subassemblies for 28% lower workload proxy effort with fidelity within 1% of global updating on experimental flexible wing data.
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Semantic Allocation in Ordered Bottlenecks: Predictive Residual Inference for Visual Representation Learning
PRIOR replaces masking-based ordering pressure with predictive residual inference using level-wise predictors to produce well-ordered representations that maintain or improve performance across budgets, especially in discrete settings.
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GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization
LLMs can forecast GPU kernel performance accurately enough to serve as selective surrogates, allowing kernel searches to consider more candidates and recover faster kernels under fixed GPU evaluation budgets.
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Bayesian Algorithm for Collaborative Optimization with Application to Aircraft Design
BACO replaces direct black-box calls in collaborative optimization with Gaussian process surrogates at both subsystem and system levels, achieving lower objectives and near-zero constraint violations on MDO benchmarks and a CRM wing problem within limited evaluations.
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A Framework for Nonlinearly-Constrained Gradient-Enhanced Local Bayesian Optimization with Comparisons to Quasi-Newton Optimizers
Two frameworks for nonlinear equality constraints in gradient-enhanced local Bayesian optimization achieve deeper convergence with fewer function evaluations than previous constrained BO methods and SciPy/MATLAB quasi-Newton optimizers on unimodal problems with 2-30 variables.
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Surrogate Assisted Pedestrian Protection Design via a Foundation Model Orchestrated Workflow
Describes an LLM-orchestrated workflow that trains a surrogate on CAE data (R²=0.87), runs NSGA-II optimization, generates morphed geometries, and produces 35 compliant pedestrian-protection designs in a front-bumper case study.
-
Efficient Gradient-Enhanced Bayesian Optimizer with Comparisons to Conjugate-Gradient and Quasi-Newton Optimizers for Unconstrained Local Optimization
A gradient-enhanced local Bayesian optimization framework that converges optimality as deeply as standard optimizers but with significantly fewer function evaluations on 2-40 dimensional unimodal problems, outperforming them under noisy gradients.
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Hyp-RL : Hyperparameter Optimization by Reinforcement Learning
Reinforcement learning selects hyperparameters sequentially by learning from actual future validation loss reductions and outperforms SMBO methods on 50 datasets.
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Monte Carlo Integration with adaptive variance selection for improved stochastic Efficient Global Optimization
An adaptive target-variance scheme for Monte Carlo integration inside sEGO improves performance over fixed-variance and multi-start baselines on stochastic benchmarks and a tuned-mass-damper problem while enabling high stochastic dimension counts.
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Derivative-free optimization is competitive for aerodynamic design optimization in moderate dimensions
Systematic benchmarks on NACA0012, RAE2822, and ONERA M6 cases show derivative-free optimizers competitive with adjoint-based methods and stronger in higher dimensions.
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A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches
Bayesian optimization with Gaussian processes unifies minimization, single-point saddle searches, and double-ended path searches on potential energy surfaces through a shared six-step surrogate loop using derivative observations and inverse-distance kernels.
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Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
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