Zobrazeno 1 - 10
of 1 256
pro vyhledávání: '"J Buehler"'
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
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
Publikováno v:
Frontiers in Psychiatry, Vol 14 (2023)
BackgroundWhile online reviews from physician rating websites are increasingly utilized by healthcare providers to better understand patient needs, it remains difficult to objectively identify areas for improvement in providing psychiatric care.Objec
Externí odkaz:
https://doaj.org/article/0e264256462e4846b2129ae137702370
Autor:
Yiwen Hu, Markus J. Buehler
Publikováno v:
APL Machine Learning, Vol 1, Iss 1, Pp 010901-010901-18 (2023)
Machine learning (ML) has emerged as an indispensable methodology to describe, discover, and predict complex physical phenomena that efficiently help us learn underlying functional rules, especially in cases when conventional modeling approaches cann
Externí odkaz:
https://doaj.org/article/6eb566f97530436fa5e558de80d331b2
Autor:
Sheng Gong, Shuo Wang, Taishan Zhu, Xi Chen, Zhenze Yang, Markus J. Buehler, Yang Shao-Horn, Jeffrey C. Grossman
Publikováno v:
JACS Au, Vol 1, Iss 11, Pp 1904-1914 (2021)
Externí odkaz:
https://doaj.org/article/c6d0bb408c5a4ae29018a99ee76ac09b