Molecular Dynamics Probability And Uncertainty
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Molecular Dynamics: Probability and Uncertainty
Author: Peter V. Coveney
language: en
Publisher: Oxford University Press
Release Date: 2025-05-15
Embark on a fascinating exploration of molecular dynamics, which combines the authors' new probabilistic interpretation with cutting-edge simulations, some of which are performed on the largest supercomputers on our planet. From fundamental principles to innovative applications, this book covers the rich tapestry of molecular dynamics and its intersections with biological and medical sciences, materials science and engineering, and artificial intelligence, alongside uncertainty quantification. This enables the authors to highlight the critical role of molecular dynamics in delivering actionable outcomes for drug discovery, materials design and beyond. Beginning with a solid introduction to the intricate world of molecular dynamics, the book goes on to describe its modern probabilistic formulation. It investigates ensemble-based molecular dynamics simulations and free energies, uncovering both the way that ensemble techniques revolutionize simulation methodologies and how they empower researchers to generate new insights. Further, the book explores the exciting realm of simulations for advanced materials and discusses verification, validation and uncertainty quantification, illuminating the synergies between molecular dynamics and artificial intelligence and their potential for transformative breakthroughs. Whether you are a seasoned researcher seeking to expand your knowledge or a curious student eager to investigate the complexities of molecular dynamics, this book serves as an indispensable resource, challenging conventional approaches, offering fresh perspectives and unlocking new insights into real-world problems in this captivating field.
Molecular Dynamics: Probability and Uncertainty
Dive into the captivating realm of molecular dynamics with this comprehensive guide, offering a unique blend of classic techniques and cutting-edge probabilistic formulations. In this insightful journey, we explore the fundamental principles, advanced applications, and emerging frontiers shaping the field today.
Uncertainty Quantification and Model Calibration
Author: Jan Peter Hessling
language: en
Publisher: BoD – Books on Demand
Release Date: 2017-07-05
Uncertainty quantification may appear daunting for practitioners due to its inherent complexity but can be intriguing and rewarding for anyone with mathematical ambitions and genuine concern for modeling quality. Uncertainty quantification is what remains to be done when too much credibility has been invested in deterministic analyses and unwarranted assumptions. Model calibration describes the inverse operation targeting optimal prediction and refers to inference of best uncertain model estimates from experimental calibration data. The limited applicability of most state-of-the-art approaches to many of the large and complex calculations made today makes uncertainty quantification and model calibration major topics open for debate, with rapidly growing interest from both science and technology, addressing subtle questions such as credible predictions of climate heating.