Machine learning for data-driven discovery in solid Earth geoscience
BACKGROUND The solid Earth, oceans, and atmosphere together form a complex
interacting geosystem. Processes relevant to understanding Earth's geosystem behavior …
interacting geosystem. Processes relevant to understanding Earth's geosystem behavior …
Machine learning and physics: A survey of integrated models
A Seyyedi, M Bohlouli, SN Oskoee - ACM Computing Surveys, 2023 - dl.acm.org
Predictive modeling of various systems around the world is extremely essential from the
physics and engineering perspectives. The recognition of different systems and the capacity …
physics and engineering perspectives. The recognition of different systems and the capacity …
Theory-guided data science: A new paradigm for scientific discovery from data
A Karpatne, G Atluri, JH Faghmous… - … on knowledge and …, 2017 - ieeexplore.ieee.org
Data science models, although successful in a number of commercial domains, have had
limited applicability in scientific problems involving complex physical phenomena. Theory …
limited applicability in scientific problems involving complex physical phenomena. Theory …
Industrial big data analytics: challenges, methodologies, and applications
JP Wang, WS Zhang, YK Shi, SH Duan… - arXiv preprint arXiv …, 2018 - arxiv.org
While manufacturers have been generating highly distributed data from various systems,
devices and applications, a number of challenges in both data management and data …
devices and applications, a number of challenges in both data management and data …
Tensor analysis and fusion of multimodal brain images
E Karahan, PA Rojas-Lopez… - Proceedings of the …, 2015 - ieeexplore.ieee.org
Current high-throughput data acquisition technologies probe dynamical systems with
different imaging modalities, generating massive data sets at different spatial and temporal …
different imaging modalities, generating massive data sets at different spatial and temporal …
Physics-guided deep learning for drag force prediction in dense fluid-particulate systems
Physics-based simulations are often used to model and understand complex physical
systems in domains such as fluid dynamics. Such simulations, although used frequently …
systems in domains such as fluid dynamics. Such simulations, although used frequently …
Geophysical inversion with convolutional neural networks
H Denli, NA Subrahmanya - US Patent 10,996,372, 2021 - Google Patents
A method including: storing, in a computer memory, geophysical data obtained from a
survey of a subsurface region; and extracting, with a computer, a subsurface physical …
survey of a subsurface region; and extracting, with a computer, a subsurface physical …
A framework for glass-box physics rule learner and its application to nano-scale phenomena
Attempts to use machine learning to discover hidden physical rules are in their infancy, and
such attempts confront more challenges when experiments involve multifaceted …
such attempts confront more challenges when experiments involve multifaceted …
[HTML][HTML] Structure optimization of prior-knowledge-guided neural networks
M Atwya, G Panoutsos - Neurocomputing, 2022 - Elsevier
Prior-knowledge use in neural networks, for example, knowledge of a physical system,
allows network training to be tailored to specific problems. Literature shows that prior …
allows network training to be tailored to specific problems. Literature shows that prior …