Zobrazeno 1 - 10
of 11 758
pro vyhledávání: '"J Buehler"'
Publikováno v:
Shape Memory and Superelasticity; 20240101, Issue: Preprints p1-2, 2p
Autor:
Markus J. Buehler, Tomás Saraceno
Publikováno v:
Active Matter ISBN: 9780262342476
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d28d0205d5581c87c01c50bb83afceee
https://doi.org/10.7551/mitpress/11236.003.0009
https://doi.org/10.7551/mitpress/11236.003.0009
Autor:
Markus J. Buehler
Publikováno v:
ACS Engineering Au, Vol 4, Iss 2, Pp 241-277 (2024)
Externí odkaz:
https://doaj.org/article/5f9a06c70f4e4e83bd99628b14e4ad98
Autor:
Eric L. Buehler, Markus J. Buehler
Publikováno v:
APL Machine Learning, Vol 2, Iss 2, Pp 026119-026119-41 (2024)
We report a mixture of expert strategy to create fine-tuned large language models using a deep layer-wise token-level approach based on low-rank adaptation (LoRA). Starting with a set of pre-trained LoRA adapters, our gating strategy uses the hidden
Externí odkaz:
https://doaj.org/article/3b0136e248e54c8b9230cc013fa5f19f
Autor:
Rachel K. Luu, Markus J. Buehler
Publikováno v:
Advanced Science, Vol 11, Iss 10, Pp n/a-n/a (2024)
Abstract The study of biological materials and bio‐inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open‐s
Externí odkaz:
https://doaj.org/article/67448cdaf200409685d0a93ba0769386
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-9 (2023)
Abstract Architected materials can achieve enhanced properties compared to their plain counterparts. Specific architecting serves as a powerful design lever to achieve targeted behavior without changing the base material. Thus, the connection between
Externí odkaz:
https://doaj.org/article/aaf92251d706482bb22f63b78e304f82
Akademický článek
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Autor:
Markus J Buehler
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035083 (2024)
Leveraging generative Artificial Intelligence (AI), we have transformed a dataset comprising 1000 scientific papers focused on biological materials into a comprehensive ontological knowledge graph. Through an in-depth structural analysis of this grap
Externí odkaz:
https://doaj.org/article/344846022cb5461682016922378dda93
Autor:
Zhenze Yang, Markus J. Buehler
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-13 (2022)
Abstract Structural defects are abundant in solids, and vital to the macroscopic materials properties. However, a defect-property linkage typically requires significant efforts from experiments or simulations, and often contains limited information d
Externí odkaz:
https://doaj.org/article/7c44f3ab48e54e8c9ebc7b7f493f217a
Autor:
Yu-Chuan Hsu, Markus J. Buehler
Publikováno v:
APL Machine Learning, Vol 1, Iss 2, Pp 026105-026105-10 (2023)
The dynamics of material failure is a critical phenomenon relevant to a range of scientific and engineering fields, from healthcare to structural materials. We propose a specially designed deep neural network, DyFraNet, which can predict dynamic frac
Externí odkaz:
https://doaj.org/article/29ed8b8ee23849608eff49b7b7594304