A multi-source extension of constrained Max-value Entropy Search for Bayesian optimization incorporates auxiliary data sources to improve early exploration and performance under constraints even with weak correlations.
and Pleiss, Geoff and Bindel, David and Weinberger, Kilian Q
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Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.
MAESTRO couples surrogate optimization transport modeling with external solvers to enable efficient full-physics steady-state plasma predictions in fusion devices.
A Gaussian Process Regression model trained on an archive of eccentricity-reduced binary black hole simulations predicts initial conditions that achieve low eccentricity with zero or one iteration.
Multi-fidelity bandits screen 529 ZnO co-dopants with 81% fewer DFT calls, identify Y2Cu2 co-doped ZnO (1.84 eV) as optimal for visible-light band gaps, and release all 583 calculations plus code.
A trust-region Bayesian optimization framework integrates LEED multiple scattering models to jointly optimize structural and experimental parameters for automated surface reconstruction.
Tutorial on a GP-based framework for preference and choice learning that unifies random utility models, limits of discernment, and multi-utility scenarios via customized likelihoods for object and label preferences.
A comprehensive review of deep learning techniques for computational mechanics, including LSTM for constitutive modeling, PINNs for PDE solving, optimizers, and kernel methods.
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Physics-informed automated surface reconstructing via low-energy electron diffraction based on Bayesian optimization
A trust-region Bayesian optimization framework integrates LEED multiple scattering models to jointly optimize structural and experimental parameters for automated surface reconstruction.