Zobrazeno 1 - 10
of 361
pro vyhledávání: '"Frank Emmert‐Streib"'
Publikováno v:
AI, Vol 5, Iss 3, Pp 1534-1557 (2024)
Physics-informed neural networks (PINNs) represent a significant advancement at the intersection of machine learning and physical sciences, offering a powerful framework for solving complex problems governed by physical laws. This survey provides a c
Externí odkaz:
https://doaj.org/article/cc78f87bc6c74aa381158c9a4bc72e08
Autor:
Frank Emmert-Streib
Publikováno v:
Heliyon, Vol 10, Iss 21, Pp e40133- (2024)
Null hypothesis significance testing (NHST) is among the most prominent and widely used methods for analyzing data. At the same time, NHST has been criticized since many years because of misuses and misconceptions that can be found extensively in the
Externí odkaz:
https://doaj.org/article/bf90c9d118e74e9f91af4fc2a726bb39
Autor:
Frank Emmert-Streib
Publikováno v:
Discover Artificial Intelligence, Vol 4, Iss 1, Pp 1-8 (2024)
Abstract The success of the conversational AI system ChatGPT has triggered an avalanche of studies that explore its applications in research and education. There are also high hopes that, in addition to such particular usages, it could lead to artifi
Externí odkaz:
https://doaj.org/article/91a4a2c42cb7477abe85c8ad7ad54e2b
Publikováno v:
IEEE Access, Vol 12, Pp 153253-153272 (2024)
Scientific machine learning and physics-informed neural networks are novel conceptual approaches that integrate scientific knowledge with methods from data science and deep learning. This emerging field has garnered increasing interest due to its uni
Externí odkaz:
https://doaj.org/article/acba6d585ea54063b0297c2db3bbeb15
Publikováno v:
Frontiers in Molecular Biosciences, Vol 11 (2024)
Externí odkaz:
https://doaj.org/article/c5ad0bf95d4a4a33a1b4398d1b8d810e
Publikováno v:
Heliyon, Vol 10, Iss 5, Pp e26973- (2024)
The COVID-19 pandemic presented an unparalleled challenge to global healthcare systems. A central issue revolves around the urgent need to swiftly amass critical biological and medical knowledge concerning the disease, its treatment, and containment.
Externí odkaz:
https://doaj.org/article/258c391691c94ce9ada6e713b3cc1faa
Autor:
Frank Emmert-Streib
Publikováno v:
AI, Vol 4, Iss 3, Pp 721-728 (2023)
The concept of a digital twin is intriguing as it presents an innovative approach to solving numerous real-world challenges. Initially emerging from the domains of manufacturing and engineering, digital twin research has transcended its origins and n
Externí odkaz:
https://doaj.org/article/ee9971248d82448ba26da563a598a430
Autor:
Frank Emmert-Streib
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 5, Iss 3, Pp 1036-1054 (2023)
The concept of a digital twin (DT) has gained significant attention in academia and industry because of its perceived potential to address critical global challenges, such as climate change, healthcare, and economic crises. Originally introduced in m
Externí odkaz:
https://doaj.org/article/f4c854c877c74c6cb5e3c29b347027bb
Autor:
Zhen Yang, Frank Emmert-Streib
Publikováno v:
IEEE Access, Vol 11, Pp 93402-93419 (2023)
Text data in the form of natural language is a valuable resource that contains domain-specific information applicable to various applications. An example are electronic health records (eHR) offering comprehensive insights into patients’ health hist
Externí odkaz:
https://doaj.org/article/99d4626df8dc4728abef0fa03bd659c5
Publikováno v:
IEEE Access, Vol 11, Pp 69649-69666 (2023)
Currently, studies involving a digital twin are gaining widespread interest. While the first fields adopting such a concept were in manufacturing and engineering, lately, interest extends also beyond these fields across all academic disciplines. Give
Externí odkaz:
https://doaj.org/article/616dc3946af34064bac5ba43d4f0b030