4 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…
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…
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…