How can we reconstruct the past from the present when evolution obscures the information we need? Machine Learning Evolution Equations connects the analysis of partial differential equations with numerical simulation and machine learning to investigate this question. Through models of diffusion, phase separation, fluid flow, and wave propagation, the book develops methods for learning inverse reconstructions from simulated data. Reconstruction accuracy, physical consistency, and statistical validation provide a framework for assessing what these models can recover. Written for graduate students and researchers, it links rigorous mathematics with reproducible computational experiments.
| ISBN: | 9783111705064 |
| Publication date: | 22nd March 2027 |
| Author: | Joseph L Shomberg |
| Publisher: | De Gruyter |
| Format: | Paperback |
| Pagination: | 420 pages |
| Series: | De Gruyter Textbook |
| Genres: |
Differential calculus and equations Process engineering technology and techniques Algorithms and data structures |
How can we reconstruct the past from the present when evolution obscures the information we need? Machine Learning Evolution Equations connects the analysis of partial differential equations with numerical simulation and machine learning to investigate this question.
Machine Learning Evolution Equations features in the following genres: Differential calculus and equations, Process engineering technology and techniques, Algorithms and data structures
Paperback. £61.65, down from the £68.50 cover price. Not Available.
Machine Learning Evolution Equations was written by Joseph L Shomberg and published by De Gruyter
Machine Learning Evolution Equations has 420 pages
Yes it is part of De Gruyter Textbook series
£61.65, reduced from £68.50. Not Available.