Machine Learning for Mathematical Modeling and Computation

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13

authors

4

articles

Editors

3

University of Minnesota Twin Cities

Purdue University

Peking University

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Articles of data augmentation using GANs for images and biosignals in medicine and neuroscience.

Mini Review

12 May 2023

Data augmentation using generative adversarial networks for images and biomarkers in medicine and neuroscience

Maizan Syamimi Meor Yahaya

and

Jason Teo

9,854 views

12 citations

The exchange rate of the US dollar-ID rupiah of (A). Actual data and (B). Stationary data.

Original Research

17 April 2023

The soft computing FFNN method for adjusting heteroscedasticity on the time series model of currency exchange rate

Dodi Devianto

, 2 more and

Ferra Yanuar

1,677 views

6 citations

mKdV-Burgers equation of Example 3.5: the CANN solution (red circles) and the exact solution (solid blue lines) at T = 1.0. (Left) Different values of θ with a = 1 and b = 1; (Middle) Different values of b with a = 1 and θ = 0; (Right) Different values of a with b = 1 and θ = 0.

Original Research

07 November 2022

Cell-average based neural network method for third order and fifth order KdV type equations

Yongsheng Chen

, 1 more and

Xinghui Zhong

2,784 views

3 citations

Original Research

03 August 2023

A combination of physics-informed neural networks with the fixed-stress splitting iteration for solving Biot's model

Mingchao Cai

, 2 more and

Jingzhi Li

4,220 views

1 citations

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About

Scientific machine learning is at the core of modern computational technology; it has the power to potentially transform research in science and engineering. While machine learning methods have been much used with success, there are still tremendous challenges and opportunities for increasing the scale, rigor, robustness, and reliability of such methods for practical applications.

The goal of this Research Topic is to collect recent state-of-the-art research in advancing the fundamental understanding of scientific machine learning, as well as its applications in enhancing modeling and simulation. Expected contributions include, but are not limited to:

1) leveraging domain knowledge, such as physical principles and invariance structures, to machine learning models to improve its accuracy and defensibility, as well as accelerating the model training;

2) incorporating statistics, uncertainty quantification, and probabilistic modeling into scientific machine learning to deal with large scale complex models and data;

3) improving upon current numerical solvers through the judicious use of machine learning algorithms and developing new machinery to optimally manage the interplay between traditional and machine learning models.

Scientific machine learning is at the core of modern computational technology; it has the power to potentially transform research in science and engineering. While machine learning methods have been much used with success, there are still tremendous challenges and opportunities for increasing the scale, rigor, robustness, and reliability of such methods for practical applications.

The goal of this Research Topic is to collect recent state-of-the-art research in advancing the fundamental understanding of scientific machine learning, as well as its applications in enhancing modeling and simulation. Expected contributions include, but are not limited to:

1) leveraging domain knowledge, such as physical principles and invariance structures, to machine learning models to improve its accuracy and defensibility, as well as accelerating the model training;

2) incorporating statistics, uncertainty quantification, and probabilistic modeling into scientific machine learning to deal with large scale complex models and data;

3) improving upon current numerical solvers through the judicious use of machine learning algorithms and developing new machinery to optimally manage the interplay between traditional and machine learning models.

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Editors

University of Minnesota Twin Cities

Purdue University

Peking University

Impact

19,901 Total views

16,570 Article views

2,028 Article downloads

1,303 Topic views

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Frontiers in Applied Mathematics and Statistics

Mathematics of Computation and Data Science

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