Symbolic regression in materials science
The authors showcase the potential of symbolic regression as an analytic method for use in
materials research. First, the authors briefly describe the current state-of-the-art method …
materials research. First, the authors briefly describe the current state-of-the-art method …
[HTML][HTML] Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data
Data-driven discovery of partial differential equations (PDEs) has attracted increasing
attention in recent years. Although significant progress has been made, certain unresolved …
attention in recent years. Although significant progress has been made, certain unresolved …
Bayesian learning of coupled biogeochemical–physical models
A Gupta, PFJ Lermusiaux - Progress in Oceanography, 2023 - Elsevier
Predictive dynamical models for marine ecosystems are used for a variety of needs. Due to
the sparse measurements and limited understanding of the myriad of ocean processes …
the sparse measurements and limited understanding of the myriad of ocean processes …
A robust framework for identification of PDEs from noisy data
Robust physics (eg, governing equations and laws) discovery is of great interest for many
engineering fields and explainable machine learning. A critical challenge compared with …
engineering fields and explainable machine learning. A critical challenge compared with …
Parsimony-enhanced sparse Bayesian learning for robust discovery of partial differential equations
Robust physics discovery is of great interest for many scientific and engineering fields.
Inspired by the principle that a representative model is the simplest one among all possible …
Inspired by the principle that a representative model is the simplest one among all possible …
Robust data-driven discovery of partial differential equations under uncertainties
Robust physics (eg, governing equations and laws) discovery is of great interest for many
engineering fields and explainable machine learning. A critical challenge compared with …
engineering fields and explainable machine learning. A critical challenge compared with …
Deep learning discovery of macroscopic governing equations for viscous gravity currents from microscopic simulation data
Although deep learning has been successfully applied in a variety of science and
engineering problems owing to its strong high-dimensional nonlinear mapping capability, it …
engineering problems owing to its strong high-dimensional nonlinear mapping capability, it …
Data-Driven Functional Materials Design and Discovery
Y Wang - 2021 - search.proquest.com
Functional electronic materials have transformed modern society toward a highly digitized
and interconnected global community. The ever-growing demand for electronic devices with …
and interconnected global community. The ever-growing demand for electronic devices with …