2 citations · 2 across the 4 of their papers we have counts for
5 papers
An Online Machine Learning Multi-resolution Optimization Framework for Energy System Design Limit of Performance Analysis
Oluwamayowa O. Amusat, Luka Grbcic, Remi Patureau +3
Designing reliable integrated energy systems for industrial processes requires optimization and verification models across multiple fidelities, from architecture-level sizing to hi…
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…
AI Driven Laser Parameter Search: Inverse Design of Photonic Surfaces using Greedy Surrogate-based Optimization
Luka Grbcic, Minok Park, Juliane Müller +2
Photonic surfaces designed with specific optical characteristics are becoming increasingly important for use in in various energy harvesting and storage systems. , In this study, w…
Inverse design of photonic surfaces on Inconel via multi-fidelity machine learning ensemble framework and high throughput femtosecond laser processing
Luka Grbcic, Minok Park, Mahmoud Elzouka +6
We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using…
Efficient Inverse Design Optimization through Multi-fidelity Simulations, Machine Learning, and Search Space Reduction Strategies
Luka Grbcic, Juliane Müller, Wibe Albert de Jong
This paper introduces a methodology designed to augment the inverse design optimization process in scenarios constrained by limited compute, through the strategic synergy of multi-…