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Computational Sciences and Artificial Intelligence in Industry

Part of the Intelligent Systems, Control and Automation: Science and Engineering Book Series (volume 76)

Editors: Tuovinen, Tero; Periaux, Jacques; Neittaanmäki, Pekka

DOI: 10.1007/978-3-030-70787-3

ISBN: 978-3-030-70786-6

Publisher: Springer International Publishing - Springer Journals

Aug 20, 2021

1 - 10 of 17 Chapters

Available

Co-development of Methodology, Applications, and Hardware in Computational Science and Artificial Intelligence

Neittaanmäki, Pekka; Savonen, Matti; Periaux, Jacques; Tuovinen, Tero

Pages: 3 –8

[In this introductory chapter of the book, we outline the development of computational science, artificial intelligence, and data science as part of science paradigm development. Computational science and artificial intelligence have mainly been developed separately during the past 60 years. We...
Available

Novel Strategies for Data-Driven Evolutionary Optimization

Roy, Swagata; Chakraborti, Nirupam

Pages: 11 –25

[Novel learning algorithms like Evolutionary Neural Net (EvoNN), Bi-objective Genetic Programming (BioGP), and Evolutionary Deep Neural Net (EvoDN2) developed in our laboratory are being widely used in diverse areas of engineering metamodeling and multi-objective optimization of practical...
Available

Artificial Intelligence and Computational Science

Neittaanmäki, Pekka; Repin, Sergey

Pages: 27 –35

[In this note, we discuss the interaction between two ways of scientific analysis. The first (classical) way is known as Mathematical Modeling (MM). It is based on a model created by humans and presented in mathematical terms. Scientific Computing (SC) is an important tool of MM developed to...
Available

Supervised Learning and Applied Mathematics

Pironneau, Olivier

Pages: 37 –46

[Supervised Learning is an important tool of Artificial Intelligence centered around the use of Artificial Neural Networks. Although the achievements of AI for real-life applications are undeniable, there is still much to say about its theoretical background. Conversely, old problems of applied...
Available

Application of the Topological Gradient to Parsimonious Neural Networks

Bashtova, Kateryna; Causse, Mathieu; James, Cameron; Masmoudi, Florent; Masmoudi, Mohamed; Turki, Houcine; Wolff, Joshua

Pages: 47 –61

[In this paper, a new deep learning approach based on native parsimony using the topological gradient method is introduced. This approach reduces the number of links to be identified by the learning process by several orders of magnitude and consequently, the required amount of learning data is...
Available

Generation of Error Indicators for Partial Differential Equations by Machine Learning Methods

Muzalevskiy, Alexey; Neittaanmäki, Pekka; Repin, Sergey

Pages: 63 –96

[Computer simulation methods for models based on partial differential equations usually apply adaptive strategies that generate sequences of approximations for consequently refined meshes. In this process, error indicators play a crucial role because a new (refined) mesh is created by analysis of...
Available

Newton Method for Minimal Learning Machine

Hämäläinen, Joonas; Kärkkäinen, Tommi

Pages: 97 –108

[Minimal Learning Machine (MLM) is a distance-based supervised machine learning method for classification and regression problems. Its main advances are simple formulation and fast learning. Computing the MLM prediction in regression requires a solution to the optimization problem, which is...
Available

Limited Memory Bundle Method for Clusterwise Linear Regression

Karmitsa, Napsu; Bagirov, Adil M.; Taheri, Sona; Joki, Kaisa

Pages: 109 –122

[A clusterwise linear regression problem consists of finding a number of linear functions each approximating a subset of the given data. In this paper, the limited memory bundle method is modified and combined with the incremental approach to solve this problem using its nonsmooth optimization...
Available

Improving Clustering and Cluster Validation with Missing Data Using Distance Estimation Methods

Niemelä, Marko; Kärkkäinen, Tommi

Pages: 123 –133

[Missing data introduces a challenge in the field of unsupervised learning. In clustering, when the form and the number of clusters are to be determined, one needs to deal with the missing values both in the clustering process and in the cluster validation. In the previous research, the...
Available

Kubelka–Munk Model and Stochastic Model Comparison in Skin Physical Parameter Retrieval

Annala, Leevi; Pölönen, Ilkka

Pages: 137 –151

[In the medical field, there is a need for non-invasive diagnostic tools. One particular research area is skin cancer diagnostics. Here, we study Kubelka–Munk model and stochastic skin reflectance model, which we combined from two sources to better reflect the physical structure of the skin. Our...

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