Zobrazeno 1 - 10
of 370
pro vyhledávání: '"Mueller, Juliane"'
Autor:
Ha, Yunsoo, Mueller, Juliane
Bi-fidelity stochastic optimization is increasingly favored for streamlining optimization processes by employing a cost-effective low-fidelity (LF) function, with the goal of optimizing a more expensive high-fidelity (HF) function. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2408.04625
Photonic surfaces designed with specific optical characteristics are becoming increasingly important for use in in various energy harvesting and storage systems. , In this study, we develop a surrogate-based optimization approach for designing such s
Externí odkaz:
http://arxiv.org/abs/2407.03356
Autor:
Grbcic, Luka, Park, Minok, Elzouka, Mahmoud, Prasher, Ravi, Müller, Juliane, Grigoropoulos, Costas P., Lubner, Sean D., Zorba, Vassilia, de Jong, Wibe Albert
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 high throughput femtosecond laser processing. The MF ensemble combine
Externí odkaz:
http://arxiv.org/abs/2406.01471
Autor:
Egan, Hilary, Griffin, Kevin Patrick, de Frahan, Marc T. Henry, Mueller, Juliane, Vaidhynatha, Deepthi, Wald, Dylan, Chintala, Rohit, Doronina, Olga A., King, Ryan, Sanyal, Jibonananda, Day, Marc
Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocation adaptiv
Externí odkaz:
http://arxiv.org/abs/2404.00053
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-fidelity evaluations, machine learning models, and optimization algori
Externí odkaz:
http://arxiv.org/abs/2312.03654
Autor:
Mendoza, Rey, Nguyen, Minh, Zhu, Judith Weng, Dumont, Vincent, Perciano, Talita, Mueller, Juliane, Ganapati, Vidya
Computed tomography has propelled scientific advances in fields from biology to materials science. This technology allows for the elucidation of 3-dimensional internal structure by the attenuation of x-rays through an object at different rotations re
Externí odkaz:
http://arxiv.org/abs/2211.00002
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 events in High Energy Physics (HEP), including simulating detector resp
Externí odkaz:
http://arxiv.org/abs/2208.07715
Hybrid variational quantum algorithms, which combine a classical optimizer with evaluations on a quantum chip, are the most promising candidates to show quantum advantage on current noisy, intermediate-scale quantum (NISQ) devices. The classical opti
Externí odkaz:
http://arxiv.org/abs/2204.07331
Autor:
Pion-Tonachini, Luca, Bouchard, Kristofer, Martin, Hector Garcia, Peisert, Sean, Holtz, W. Bradley, Aswani, Anil, Dwivedi, Dipankar, Wainwright, Haruko, Pilania, Ghanshyam, Nachman, Benjamin, Marrone, Babetta L., Falco, Nicola, Prabhat, Arnold, Daniel, Wolf-Yadlin, Alejandro, Powers, Sarah, Climer, Sharlee, Jackson, Quinn, Carlson, Ty, Sohn, Michael, Zwart, Petrus, Kumar, Neeraj, Justice, Amy, Tomlin, Claire, Jacobson, Daniel, Micklem, Gos, Gkoutos, Georgios V., Bickel, Peter J., Cazier, Jean-Baptiste, Müller, Juliane, Webb-Robertson, Bobbie-Jo, Stevens, Rick, Anderson, Mark, Kreutz-Delgado, Ken, Mahoney, Michael W., Brown, James B.
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering pa
Externí odkaz:
http://arxiv.org/abs/2111.13786
Autor:
Dumont, Vincent, Garner, Casey, Trivedi, Anuradha, Jones, Chelsea, Ganapati, Vidya, Mueller, Juliane, Perciano, Talita, Kiran, Mariam, Day, Marc
Publikováno v:
2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC), 2021, pp. 81-93
We present a new software, HYPPO, that enables the automatic tuning of hyperparameters of various deep learning (DL) models. Unlike other hyperparameter optimization (HPO) methods, HYPPO uses adaptive surrogate models and directly accounts for uncert
Externí odkaz:
http://arxiv.org/abs/2110.01698