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Beschreibung
Apply AI and ML to solve complex problems across sciences
Many problems in physics, engineering, and applied sciences resist traditional modeling approaches. Artificial Intelligence in Science and Engineering: From Porous Materials to Drug Discovery presents AI and ML methods for tackling otherwise unsolvable problems in complex systems. Written by Muhammad Sahimi, who brings over 40 years of research experience to the topic, this reference spans multiple scientific domains.
The book covers AI and ML applications in hydrodynamics, porous media characterization, molecular dynamics simulation, and biological phenomena including protein folding. It addresses environmental applications and drug discovery, connecting computational methods with domain-specific challenges in fluid dynamics, materials science, and biology. Readers gain access to methods that model, predict, and optimize processes difficult to approach through conventional techniques.
Readers will also find:
* Detailed treatment of AI and ML approaches applied to complex systems in fluid dynamics and porous media research
* Coverage of molecular dynamics applications where machine learning accelerates simulation and prediction of material properties
* Methods for protein folding prediction and drug discovery leveraging current artificial intelligence and computational biology techniques
* Environmental science applications demonstrating how AI-driven modeling addresses problems resistant to traditional analytical methods
* Cross-disciplinary frameworks connecting physics, engineering, materials science, and biology through unified computational approaches
Physicists, materials scientists, engineers, computer scientists, and computational biologists will find this volume a substantive reference for applying AI and ML across their research domains. By unifying coverage of diverse complex systems under one framework, the book serves both academics and practitioners working at the intersection of computation and applied science.
Many problems in physics, engineering, and applied sciences resist traditional modeling approaches. Artificial Intelligence in Science and Engineering: From Porous Materials to Drug Discovery presents AI and ML methods for tackling otherwise unsolvable problems in complex systems. Written by Muhammad Sahimi, who brings over 40 years of research experience to the topic, this reference spans multiple scientific domains.
The book covers AI and ML applications in hydrodynamics, porous media characterization, molecular dynamics simulation, and biological phenomena including protein folding. It addresses environmental applications and drug discovery, connecting computational methods with domain-specific challenges in fluid dynamics, materials science, and biology. Readers gain access to methods that model, predict, and optimize processes difficult to approach through conventional techniques.
Readers will also find:
* Detailed treatment of AI and ML approaches applied to complex systems in fluid dynamics and porous media research
* Coverage of molecular dynamics applications where machine learning accelerates simulation and prediction of material properties
* Methods for protein folding prediction and drug discovery leveraging current artificial intelligence and computational biology techniques
* Environmental science applications demonstrating how AI-driven modeling addresses problems resistant to traditional analytical methods
* Cross-disciplinary frameworks connecting physics, engineering, materials science, and biology through unified computational approaches
Physicists, materials scientists, engineers, computer scientists, and computational biologists will find this volume a substantive reference for applying AI and ML across their research domains. By unifying coverage of diverse complex systems under one framework, the book serves both academics and practitioners working at the intersection of computation and applied science.
From Porous Materials to Drug Discovery
Details
| Verlag | Wiley-VCH |
| Ersterscheinung | 16. September 2026 |
| Maße | 24.4 cm x 17 cm |
| Format | Hardcover |
| ISBN-13 | 9783527355068 |
| Auflage | 1. Auflage |
| Seiten | 832 |
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