Data-driven Modelling and Scientific Machine Learning in Continuum Physics
This monograph takes the reader through recent advances in data-driven methods and machine learning for problems in science—specifically in continuum physics. It develops the foundations and details a number of scientific machine learning approaches to enrich current computational models of continuu...
Enregistré dans:
| Auteur principal: | |
|---|---|
| Collectivité auteur: | |
| Format: | Électronique eBook |
| Langue: | anglais |
| Publié: |
Cham :
Springer International Publishing : Imprint: Springer,
2024.
|
| Édition: | 1st ed. 2024. |
| Collection: | Interdisciplinary Applied Mathematics,
60 |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1007/978-3-031-62029-4 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Table des matières:
- Part I. Introduction and Background in Continuum Materials Physics
- Introduction
- Nonlinear Elasticity
- Phase Field Methods
- Part II. Solving Partial Differential Equations
- Finite Element Methods
- Part III. Data-driven Modelling and Scientific Machine Learning
- Reduced Order Models: Numerical Homogenization for the Elastic Response of Material Microstructures
- Surrogate Optimization
- Graph Theoretic Methods
- Scale Bridging
- Inverse Modeling and System Inference from Data
- Machine Learning Solvers of Partial Differential Equations
- An Outlook on Scientific Machine Learning in Continuum Physics
- References.