Machine Learning Evolution Equations

Inverse Reconstruction from Simulated Data
1. Auflage 2027
ISBN: 978-3-11-170506-4
Verlag: De Gruyter

Buch, Englisch, 410 Seiten, Format (B × H): 170 mm x 240 mm

Reihe: De Gruyter Textbook

How can we reconstruct the past from the present when evolution obscures the information we need? 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.

Autoren/Hrsg.

Shomberg, Joseph L.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Chemische Verfahrenstechnik
• Mathematik | Informatik Mathematik Mathematische Analysis Differentialrechnungen und -gleichungen

Über Autor(innen)

Joseph L. Shomberg is a mathematician at Providence College, Rhode Island. His research focuses on partial differential equations, numerical analysis, and machine learning for inverse problems.