Data-Driven Science and Engineering

By Steven L. Brunton, J. Nathan Kutz. 2019

1,237 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/30c9e3fcb1ead2a827f91ff5cd203aa0d8058bff

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xuhui Meng
2. Liu Yang
3. G. Em Karniadakis
4. Jian-Xun Wang
5. Luning Sun
6. Han Gao
7. Shaowu Pan
8. Jochen Garcke

In Electrical and Electronic Engineering

1. Jian-Xun Wang
2. Luning Sun
3. Xuhui Meng
4. Michael G. Kapteyn
5. Jacob V. R. Pretorius
6. Karen E. Willcox
7. G. Em Karniadakis
8. Marco F. Duarte

In Statistical and Nonlinear Physics

1. Jian-Xun Wang
2. Han Gao
3. G. Em Karniadakis
4. Luning Sun
5. Xuhui Meng
6. Liu Yang
7. Shaowu Pan
8. Paris Perdikaris

Citations in the corpus

• B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data (2020, background): “The state-of-the-art in data-driven modeling has advanced significantly recently in applications across different fields [1, 2, 3, 4, 5], due to the rapid development of machine learning and explosive growth of available data collected from different sensors (e.g., satellites, cameras, etc.).”
• A probabilistic graphical model foundation for enabling predictive digital twins at scale (2020, methodology): “edback control loop. These computational models oer valuable insights, unattainable through raw observational data alone, which can be leveraged for improved data-driven modeling and decision making [14]. This paper presented an example application of the model, focused on the experimental calibrati”
• Explainable Machine Learning for Scientific Insights and Discoveries (2019, background): “As stated by [5], Data science is not replacing mathematical physics and engineering, but is instead augmenting it for the twenty-first century, resulting in more of a renaissance than a revolution.”
• Digital twin, physics-based model, and machine learning applied to damage detection in structures (2020, background): “ond law) with closure models (e.g. constitutive model), and the later is obtained through fitting data to some mathematical structure; for instance, neural networks [5], or any other data-driven model [6, 7, 8]. Recently, lots of researches are dedicated to leverage computational models, by integrati”
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (2019, background): “In broader scientific computing and physical modeling communities, machine learning (ML) has been receiving a lot of attentions [25– 31].”
• The dominance of large-scale phase dynamics in human cortex, from delta to gamma (2026, unknown): “SVD has been described as a generalization of Fourier analysis ( Brunton and Kutz, 2022 ), in that it is used to empirically recover a set of bases that allow the original data to be represented as weighted sums of that basis.”