43 citations · 54 across the 5 of their papers we have counts for
6 papers
Multi-modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation
Ian Taylor, Juliane Mueller, Julie Bessac
As data collection and simulation capabilities advance, multi-modal learning, the task of learning from multiple modalities and sources of data, is becoming an increasingly importa…
Multi-Fidelity Stochastic Trust Region Method with Adaptive Sampling
Yunsoo Ha, Juliane Mueller
Simulation optimization is often hindered by the high cost of running simulations. Multi-fidelity methods offer a promising solution by incorporating cheaper, lower-fidelity simula…
AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems
Luka Grbcic, Juliane Müller, Wibe Albert de Jong
Inverse design in science and engineering involves determining optimal design parameters that achieve desired performance outcomes, a process often hindered by the complexity and h…
Adaptive Sampling-Based Bi-Fidelity Stochastic Trust Region Method for Derivative-Free Stochastic Optimization
Yunsoo Ha, Juliane Mueller
Bi-fidelity stochastic optimization has gained increasing attention as an efficient approach to reduce computational costs by leveraging a low-fidelity (LF) model to optimize an ex…
Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations
Vincent Dumont, Xiangyang Ju, Juliane Mueller
The Generative Adversarial Network (GAN) is a powerful and flexible tool that can generate high-fidelity synthesized data by learning. It has seen many applications in simulating e…
MATSuMoTo: The MATLAB Surrogate Model Toolbox For Computationally Expensive Black-Box Global Optimization Problems
Juliane Mueller
MATSuMoTo is the MATLAB Surrogate Model Toolbox for computationally expensive, black-box, global optimization problems that may have continuous, mixed-integer, or pure integer vari…