Data-driven science and engineering: machine learning, dynamical systems, and control

By Thomas Peters. Contemporary physics (Print), 2019

1,250 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/f3eda875e14bf933759f3b777131a4a9973537b4

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. G. Em Karniadakis
2. Liu Yang
3. Xuhui Meng
4. Steven L. Brunton
5. Bernd R. Noack
6. Petros Koumoutsakos
7. Ricardo Vinuesa
8. Maizar Raissi

In Control and Systems Engineering

1. G. Em Karniadakis
2. Petros Koumoutsakos
3. Bernd R. Noack
4. Steven L. Brunton
5. Xuhui Meng
6. Ricardo Vinuesa
7. Liu Yang
8. M. Buchhold

Citations in the corpus

• Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression (2019, methodology): “By using the l2-norm regularisation the magnitude of the nonzero coefficients is shrunk [25, 30].”
• 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.”
• PDEBENCH: An Extensive Benchmark for Scientific Machine Learning (2022, background): “An overview and taxonomy of Scientific ML developments can be found in [28, 6].”
• Digital twin, physics-based model, and machine learning applied to damage detection in structures (2020, background): “The confusion matrix [43] is presented in Table 2.”
• Machine Learning for Fluid Mechanics (2019, background): “In (2) Alternative loss functions may reflect different constraints on the learning machine such as sparsity (Hastie et al. 2009; Brunton & Kutz 2019).”
• Enhancing computational fluid dynamics with machine learning (2021, methodology): “POD is closely related to principal-component analysis (PCA) and the singular-value decomposition (SVD) [5], which are two core dimensionality-reduction techniques used in data-driven modeling.”
• Effective Theory for the Measurement-Induced Phase Transition of Dirac Fermions (2021, methodology): “The connected two-point correlation function for linear measurement problems can be solved exactly by methods developed in the context of Kalman filtering [46] in classical control theory [47, 48], also used in quantum optics [38, 49, 50].”